rag-hybrid-search-llama-3-70b-groq.ipynb

{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.10.13","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"none","dataSources":[{"sourceId":8285162,"sourceType":"datasetVersion","datasetId":4920905}],"dockerImageVersionId":30698,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":false}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"!pip install langchain rank_bm25 pypdf unstructured chromadb\n!pip install unstructured['pdf'] unstructured\n!pip install openai tiktoken langchain_groq","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","execution":{"iopub.status.busy":"2024-05-01T22:37:16.755048Z","iopub.execute_input":"2024-05-01T22:37:16.755601Z","iopub.status.idle":"2024-05-01T22:38:33.707050Z","shell.execute_reply.started":"2024-05-01T22:37:16.755542Z","shell.execute_reply":"2024-05-01T22:38:33.706076Z"},"trusted":true},"execution_count":1,"outputs":[{"name":"stdout","text":"Collecting langchain\n  Downloading langchain-0.1.17-py3-none-any.whl.metadata (13 kB)\nCollecting rank_bm25\n  Downloading rank_bm25-0.2.2-py3-none-any.whl.metadata (3.2 kB)\nRequirement already satisfied: pypdf in /opt/conda/lib/python3.10/site-packages (4.2.0)\nCollecting unstructured\n  Downloading unstructured-0.13.6-py3-none-any.whl.metadata (30 kB)\nCollecting chromadb\n  Downloading chromadb-0.5.0-py3-none-any.whl.metadata (7.3 kB)\nRequirement already satisfied: PyYAML>=5.3 in 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langchain-text-splitters<0.1,>=0.0.1 (from langchain)\n  Downloading langchain_text_splitters-0.0.1-py3-none-any.whl.metadata (2.0 kB)\nCollecting langsmith<0.2.0,>=0.1.17 (from langchain)\n  Downloading langsmith-0.1.52-py3-none-any.whl.metadata (13 kB)\nRequirement already satisfied: numpy<2,>=1 in /opt/conda/lib/python3.10/site-packages (from langchain) (1.26.4)\nRequirement already satisfied: pydantic<3,>=1 in /opt/conda/lib/python3.10/site-packages (from langchain) (2.5.3)\nRequirement already satisfied: requests<3,>=2 in /opt/conda/lib/python3.10/site-packages (from langchain) (2.31.0)\nRequirement already satisfied: tenacity<9.0.0,>=8.1.0 in /opt/conda/lib/python3.10/site-packages (from langchain) (8.2.3)\nRequirement already satisfied: typing_extensions>=4.0 in /opt/conda/lib/python3.10/site-packages (from pypdf) (4.9.0)\nCollecting chardet (from unstructured)\n  Downloading chardet-5.2.0-py3-none-any.whl.metadata (3.4 kB)\nCollecting filetype (from unstructured)\n  Downloading filetype-1.2.0-py2.py3-none-any.whl.metadata (6.5 kB)\nCollecting python-magic (from unstructured)\n  Downloading python_magic-0.4.27-py2.py3-none-any.whl.metadata (5.8 kB)\nRequirement already satisfied: lxml in /opt/conda/lib/python3.10/site-packages (from unstructured) (5.2.1)\nRequirement already satisfied: nltk in /opt/conda/lib/python3.10/site-packages (from unstructured) (3.2.4)\nRequirement already satisfied: tabulate in /opt/conda/lib/python3.10/site-packages (from unstructured) (0.9.0)\nRequirement already satisfied: beautifulsoup4 in /opt/conda/lib/python3.10/site-packages (from unstructured) (4.12.2)\nRequirement already satisfied: emoji in /opt/conda/lib/python3.10/site-packages (from unstructured) (2.11.0)\nCollecting python-iso639 (from unstructured)\n  Downloading python_iso639-2024.4.27-py3-none-any.whl.metadata (13 kB)\nCollecting langdetect (from unstructured)\n  Downloading langdetect-1.0.9.tar.gz (981 kB)\n\u001b[2K     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chroma_hnswlib-0.7.3-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (252 bytes)\nRequirement already satisfied: fastapi>=0.95.2 in /opt/conda/lib/python3.10/site-packages (from chromadb) (0.108.0)\nRequirement already satisfied: uvicorn>=0.18.3 in /opt/conda/lib/python3.10/site-packages (from uvicorn[standard]>=0.18.3->chromadb) (0.25.0)\nCollecting posthog>=2.4.0 (from chromadb)\n  Downloading posthog-3.5.0-py2.py3-none-any.whl.metadata (2.0 kB)\nCollecting onnxruntime>=1.14.1 (from chromadb)\n  Downloading onnxruntime-1.17.3-cp310-cp310-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl.metadata (4.4 kB)\nRequirement already satisfied: opentelemetry-api>=1.2.0 in /opt/conda/lib/python3.10/site-packages (from chromadb) (1.22.0)\nRequirement already satisfied: opentelemetry-exporter-otlp-proto-grpc>=1.2.0 in /opt/conda/lib/python3.10/site-packages (from chromadb) (1.22.0)\nCollecting opentelemetry-instrumentation-fastapi>=0.41b0 (from chromadb)\n  Downloading 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opentelemetry_instrumentation-0.43b0-py3-none-any.whl (28 kB)\nDownloading opentelemetry_util_http-0.43b0-py3-none-any.whl (6.9 kB)\nDownloading orjson-3.10.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (142 kB)\n\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m142.7/142.7 kB\u001b[0m \u001b[31m9.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n\u001b[?25hDownloading posthog-3.5.0-py2.py3-none-any.whl (41 kB)\n\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m41.3/41.3 kB\u001b[0m \u001b[31m2.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n\u001b[?25hDownloading chardet-5.2.0-py3-none-any.whl (199 kB)\n\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m199.4/199.4 kB\u001b[0m \u001b[31m11.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n\u001b[?25hDownloading filetype-1.2.0-py2.py3-none-any.whl (19 kB)\nDownloading python_iso639-2024.4.27-py3-none-any.whl (274 kB)\n\u001b[2K   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\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m46.0/46.0 kB\u001b[0m \u001b[31m2.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n\u001b[?25hDownloading pyproject_hooks-1.1.0-py3-none-any.whl (9.2 kB)\nDownloading asgiref-3.8.1-py3-none-any.whl (23 kB)\nDownloading humanfriendly-10.0-py2.py3-none-any.whl (86 kB)\n\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m86.8/86.8 kB\u001b[0m \u001b[31m5.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n\u001b[?25hBuilding wheels for collected packages: pypika, langdetect\n  Building wheel for pypika (pyproject.toml) ... \u001b[?25ldone\n\u001b[?25h  Created wheel for pypika: filename=PyPika-0.48.9-py2.py3-none-any.whl size=53724 sha256=6d6564b4657dbd60ce893507ba20127368201eadb05b90f691a7aab7cc5243f4\n  Stored in directory: /root/.cache/pip/wheels/e1/26/51/d0bffb3d2fd82256676d7ad3003faea3bd6dddc9577af665f4\n  Building wheel for langdetect (setup.py) ... \u001b[?25ldone\n\u001b[?25h  Created wheel for langdetect: filename=langdetect-1.0.9-py3-none-any.whl size=993225 sha256=b1af010d0018040824dc409f31241c390a84e5296b8706d6f9f262183589031d\n  Stored in directory: /root/.cache/pip/wheels/95/03/7d/59ea870c70ce4e5a370638b5462a7711ab78fba2f655d05106\nSuccessfully built pypika langdetect\nInstalling collected packages: pypika, monotonic, mmh3, filetype, rapidfuzz, rank_bm25, python-magic, python-iso639, pyproject_hooks, packaging, orjson, opentelemetry-util-http, langdetect, jsonpath-python, humanfriendly, chroma-hnswlib, chardet, bcrypt, asgiref, posthog, coloredlogs, build, opentelemetry-instrumentation, onnxruntime, langsmith, kubernetes, unstructured-client, opentelemetry-instrumentation-asgi, langchain-core, unstructured, opentelemetry-instrumentation-fastapi, langchain-text-splitters, langchain-community, langchain, chromadb\n  Attempting uninstall: packaging\n    Found existing installation: packaging 21.3\n    Uninstalling packaging-21.3:\n      Successfully uninstalled packaging-21.3\n  Attempting uninstall: orjson\n    Found existing installation: orjson 3.9.10\n    Uninstalling orjson-3.9.10:\n      Successfully uninstalled orjson-3.9.10\n  Attempting uninstall: kubernetes\n    Found existing installation: kubernetes 26.1.0\n    Uninstalling kubernetes-26.1.0:\n      Successfully uninstalled kubernetes-26.1.0\n\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\nkeras-cv 0.8.2 requires keras-core, which is not installed.\nkeras-nlp 0.9.3 requires keras-core, which is not installed.\ntensorflow-decision-forests 1.8.1 requires wurlitzer, which is not installed.\napache-beam 2.46.0 requires dill<0.3.2,>=0.3.1.1, but you have dill 0.3.8 which is incompatible.\napache-beam 2.46.0 requires numpy<1.25.0,>=1.14.3, but you have numpy 1.26.4 which is incompatible.\napache-beam 2.46.0 requires pyarrow<10.0.0,>=3.0.0, but you have pyarrow 15.0.2 which is incompatible.\ngoogle-cloud-bigquery 2.34.4 requires packaging<22.0dev,>=14.3, but you have packaging 23.2 which is incompatible.\njupyterlab 4.1.6 requires jupyter-lsp>=2.0.0, but you have jupyter-lsp 1.5.1 which is incompatible.\njupyterlab-lsp 5.1.0 requires jupyter-lsp>=2.0.0, but you have jupyter-lsp 1.5.1 which is incompatible.\nkfp 2.5.0 requires google-cloud-storage<3,>=2.2.1, but you have google-cloud-storage 1.44.0 which is incompatible.\nkfp 2.5.0 requires kubernetes<27,>=8.0.0, but you have kubernetes 29.0.0 which is incompatible.\nlibpysal 4.9.2 requires shapely>=2.0.1, but you have shapely 1.8.5.post1 which is incompatible.\nmomepy 0.7.0 requires shapely>=2, but you have shapely 1.8.5.post1 which is incompatible.\nosmnx 1.9.2 requires shapely>=2.0, but you have shapely 1.8.5.post1 which is incompatible.\nspopt 0.6.0 requires shapely>=2.0.1, but you have shapely 1.8.5.post1 which is incompatible.\ntensorflow 2.15.0 requires keras<2.16,>=2.15.0, but you have keras 3.2.1 which is incompatible.\nydata-profiling 4.6.4 requires numpy<1.26,>=1.16.0, but you have numpy 1.26.4 which is incompatible.\u001b[0m\u001b[31m\n\u001b[0mSuccessfully installed asgiref-3.8.1 bcrypt-4.1.2 build-1.2.1 chardet-5.2.0 chroma-hnswlib-0.7.3 chromadb-0.5.0 coloredlogs-15.0.1 filetype-1.2.0 humanfriendly-10.0 jsonpath-python-1.0.6 kubernetes-29.0.0 langchain-0.1.17 langchain-community-0.0.36 langchain-core-0.1.48 langchain-text-splitters-0.0.1 langdetect-1.0.9 langsmith-0.1.52 mmh3-4.1.0 monotonic-1.6 onnxruntime-1.17.3 opentelemetry-instrumentation-0.43b0 opentelemetry-instrumentation-asgi-0.43b0 opentelemetry-instrumentation-fastapi-0.43b0 opentelemetry-util-http-0.43b0 orjson-3.10.2 packaging-23.2 posthog-3.5.0 pypika-0.48.9 pyproject_hooks-1.1.0 python-iso639-2024.4.27 python-magic-0.4.27 rank_bm25-0.2.2 rapidfuzz-3.8.1 unstructured-0.13.6 unstructured-client-0.22.0\nRequirement already satisfied: unstructured in /opt/conda/lib/python3.10/site-packages (0.13.6)\nRequirement already satisfied: chardet in /opt/conda/lib/python3.10/site-packages (from unstructured[pdf]) (5.2.0)\nRequirement already satisfied: filetype in /opt/conda/lib/python3.10/site-packages (from unstructured[pdf]) (1.2.0)\nRequirement already satisfied: python-magic in /opt/conda/lib/python3.10/site-packages (from unstructured[pdf]) (0.4.27)\nRequirement already satisfied: lxml in /opt/conda/lib/python3.10/site-packages 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collected packages: antlr4-python3-runtime, python-multipart, pypdfium2, portalocker, Pillow, omegaconf, unstructured.pytesseract, pillow-heif, pikepdf, iopath, pycocotools, pdfminer.six, pdfplumber, layoutparser, effdet, unstructured-inference\n  Attempting uninstall: Pillow\n    Found existing installation: Pillow 9.5.0\n    Uninstalling Pillow-9.5.0:\n      Successfully uninstalled Pillow-9.5.0\n\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\nspopt 0.6.0 requires shapely>=2.0.1, but you have shapely 1.8.5.post1 which is incompatible.\nydata-profiling 4.6.4 requires numpy<1.26,>=1.16.0, but you have numpy 1.26.4 which is incompatible.\u001b[0m\u001b[31m\n\u001b[0mSuccessfully installed Pillow-10.3.0 antlr4-python3-runtime-4.9.3 effdet-0.4.1 iopath-0.1.10 layoutparser-0.3.4 omegaconf-2.3.0 pdfminer.six-20231228 pdfplumber-0.11.0 pikepdf-8.15.1 pillow-heif-0.16.0 portalocker-2.8.2 pycocotools-2.0.7 pypdfium2-4.29.0 python-multipart-0.0.9 unstructured-inference-0.7.29 unstructured.pytesseract-0.3.12\n","output_type":"stream"}]},{"cell_type":"code","source":"import os \n\nos.environ[\"OPENAI_API_KEY\"] = \"enter your OPENAI_API_KEY\"","metadata":{"execution":{"iopub.status.busy":"2024-05-01T22:38:33.710086Z","iopub.execute_input":"2024-05-01T22:38:33.710591Z","iopub.status.idle":"2024-05-01T22:38:33.718937Z","shell.execute_reply.started":"2024-05-01T22:38:33.710545Z","shell.execute_reply":"2024-05-01T22:38:33.717750Z"},"trusted":true},"execution_count":2,"outputs":[]},{"cell_type":"markdown","source":"### Data","metadata":{}},{"cell_type":"code","source":"from langchain.document_loaders import UnstructuredPDFLoader\n\nfile_path = \"/kaggle/input/ragdata/Orca_paper.pdf\"\ndocuments = UnstructuredPDFLoader(\n    file_path\n)\n\ndocs = documents.load()","metadata":{"execution":{"iopub.status.busy":"2024-05-01T22:44:05.697536Z","iopub.execute_input":"2024-05-01T22:44:05.697920Z","iopub.status.idle":"2024-05-01T22:44:17.168061Z","shell.execute_reply.started":"2024-05-01T22:44:05.697894Z","shell.execute_reply":"2024-05-01T22:44:17.167055Z"},"trusted":true},"execution_count":3,"outputs":[]},{"cell_type":"code","source":"print(docs[0].page_content)","metadata":{"execution":{"iopub.status.busy":"2024-05-01T22:44:32.693130Z","iopub.execute_input":"2024-05-01T22:44:32.694178Z","iopub.status.idle":"2024-05-01T22:44:32.710268Z","shell.execute_reply.started":"2024-05-01T22:44:32.694136Z","shell.execute_reply":"2024-05-01T22:44:32.707893Z"},"trusted":true},"execution_count":4,"outputs":[{"name":"stdout","text":"3 2 0 2\n\nn u J\n\n5\n\n] L C . s c [\n\n1 v 7 0 7 2 0 . 6 0 3 2 : v i X r a\n\nOrca: Progressive Learning from Complex\n\nExplanation Traces of GPT-4\n\nSubhabrata Mukherjee∗†, Arindam Mitra∗\n\nGanesh Jawahar, Sahaj Agarwal, Hamid Palangi, Ahmed Awadallah\n\nMicrosoft Research\n\nAbstract\n\nRecent research has focused on enhancing the capability of smaller models through imitation learning, drawing on the outputs generated by large foundation models (LFMs). A number of issues impact the quality of these models, ranging from limited imitation signals from shallow LFM outputs; small scale homogeneous training data; and most notably a lack of rigorous evaluation resulting in overestimating the small model’s capability as they tend to learn to imitate the style, but not the reasoning process of LFMs. To address these challenges, we develop Orca, a 13-billion parameter model that learns to imitate the reasoning process of LFMs. Orca learns from rich signals from GPT-4 including explanation traces; step-by-step thought processes; and other complex instructions, guided by teacher assistance from ChatGPT. To promote this progressive learning, we tap into large-scale and diverse imitation data with judicious sampling and selection. Orca surpasses conventional state-of-the-art instruction-tuned models such as Vicuna-13B by more than 100% in complex zero-shot reasoning benchmarks like Big- Bench Hard (BBH) and 42% on AGIEval. Moreover, Orca reaches parity with ChatGPT on the BBH benchmark and shows competitive performance (4 pts gap with optimized system message) in professional and academic examinations like the SAT, LSAT, GRE, and GMAT, both in zero-shot settings without CoT; while trailing behind GPT-4. Our research indicates that learning from step-by-step explanations, whether these are generated by humans or more advanced AI models, is a promising direction to improve model capabilities and skills.\n\n∗Co-primary authors. Author contributions listed at the end of the paper. †Correspondence to subhabrata.mukherjee@microsoft.com\n\nWe are working with our legal team to publicly release a diff of the model weights in accordance with LLaMA’s release policy to be published at https://aka.ms/orca-lm.\n\nWork in progress.\n\nContents\n\n1 Introduction\n\n1.1 Challenges with Existing Methods\n\n. . . . . . . . . . . . . . . . . . . . . . . .\n\n1.2 Key Contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .\n\n2 Preliminaries\n\n2.1\n\nInstruction Tuning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .\n\n2.2 Role of System Instructions . . . . . . . . . . . . . . . . . . . . . . . . . . . .\n\n3 Explanation Tuning\n\n3.1 Dataset Construction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .\n\n3.1.1\n\nSystem Messages . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .\n\n3.1.2 Dataset Description and Sampling from the FLAN-v2 Collection . . .\n\n3.1.3 ChatGPT as Teaching Assistant\n\n. . . . . . . . . . . . . . . . . . . . . 12\n\n3.2 Training . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13\n\n4 Experiment Setup\n\n4.1 Baselines\n\n. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14\n\n4.2 Tasks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15\n\n4.2.1 Open-ended Generation Capabilities . . . . . . . . . . . . . . . . . . . 15\n\n4.2.2 Reasoning Capabilities . . . . . . . . . . . . . . . . . . . . . . . . . . . 16\n\n5 Evaluation for Open-ended Generation\n\n6 Evaluation for Reasoning\n\n6.1 AGIEval Results\n\n. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17\n\n6.2 Big-Bench Hard Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20\n\n7 Evaluation for Safety\n\n7.1 Truthful Question Answering . . . . . . . . . . . . . . . . . . . . . . . . . . . 23\n\n7.2 Toxic Content Generation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26\n\n7.3 Note on Hallucination and Tool Augmented LFMs . . . . . . . . . . . . . . . 27\n\n8 Limitations\n\n9 Conclusions\n\n10 Author Contributions\n\n11 Case Studies\n\n11.1 Trigonometric Problem Solving . . . . . . . . . . . . . . . . . . . . . . . . . . 30\n\n11.2 Temporal Reasoning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32\n\n11.3 Multiple-choice Question-Answering . . . . . . . . . . . . . . . . . . . . . . . 33\n\n2\n\n4\n\n5\n\n6\n\n7\n\n7\n\n7\n\n8\n\n8\n\n9\n\n9\n\n14\n\n17\n\n17\n\n23\n\n28\n\n29\n\n29\n\n30\n\n11.4 Bio Olympiad . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34\n\n11.5 Forming Inequalities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35\n\n11.6 Counterfactual Question Answering . . . . . . . . . . . . . . . . . . . . . . . . 38\n\n11.7 Compound Interest Problems . . . . . . . . . . . . . . . . . . . . . . . . . . . 38\n\n11.8 Question from Vicuna-Eval\n\n. . . . . . . . . . . . . . . . . . . . . . . . . . . . 39\n\n11.9 Spatial Reasoning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41\n\n11.10Commonsense Question Answering . . . . . . . . . . . . . . . . . . . . . . . . 42\n\n11.11Hallucination . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44\n\n11.12Quadratic Equation Solving . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45\n\n11.13Meeting Transcript Processing . . . . . . . . . . . . . . . . . . . . . . . . . . 46\n\n3\n\n1 Introduction\n\n6876929310010320406080100120LLaMA-13BAlpaca-13BVicuna-13BBardChatGPTOrca-13BPerformance (%) relative to ChatGPTEvaluation with GPT-4\n\nFigure 1: Orca (13B params) outperforms a wide range of foundation models including Ope- nAI ChatGPT as evaluated by GPT-4 in the Vicuna evaluation set. We further demonstrate similar results against a wide range of evaluation sets from other works in experiments.\n\n3042424720253035404550Vicuna-13BText-da-Vinci-003Orca-13BChatGPTAggregate Accuracy (%)Professional and Academic Exams (SAT, LSAT, GRE, GMAT) (Zero-shot, MCQ)\n\nFigure 2: Explanation tuning with Orca (13B params) bridges gap with OpenAI foundation models like Text-da-Vinci-003 with 5 pts gap (the gap further reduces with optimized system messages) against ChatGPT across a wide range of professional and academic exams including GRE, GMAT, LSAT, SAT from the AGIEval benchmark [1] in zero-shot settings (without any exemplar or CoT). Topical performances shown in Figure 11.\n\nLarge Foundation Models (LFMs) such as ChatGPT and GPT-4 [2] exhibit remarkable zero- shot performances across a broad spectrum of tasks. Alongside academic benchmarks like Human Eval [3] and Big Bench [4], GPT-4 has also demonstrated human-level performance on various professional exams, including the bar exam, SAT, GRE, and USMLE. These advancements can be credited to the scaling of both model and dataset sizes, as well as the incorporation of a second layer of training to better align the models with user intent. This alignment is accomplished by fine-tuning the models via supervised learning on demonstrations of prompts and desired model behavior, and through reinforcement learning from human preferences [5].\n\nAs these models continue to evolve and become more powerful, an intriguing question arises: Can we use the model itself to supervise its own behavior or that of other AI models? Bai et al. [6] have shown that by sampling output from an initial model, generating revisions, and then fine-tuning the original model based on these revised responses, model behavior can be controlled more effectively and can be made more harmless, with significantly fewer human labels.\n\nRecently, there has been an influx of studies using LFMs like ChatGPT and GPT-4 as teachers to generate large datasets, for instruction tuning, and to train smaller models, such as Alpaca [7], WizardLM [8] and Vicuna [9]. While these models can produce content that matches the style of their teachers, they often fall short in terms of the reasoning and comprehension skills displayed by the larger foundation models.\n\n4\n\n23.348.949.70102030405060Vicuna-13BChatGPTOrca-13BAggregate Accuracy (%)BigBench-Hard (Zero-shot, MCQ)\n\nFigure 3: For complex zero-shot reasoning tasks in BigBench-Hard, Orca achieves parity with ChatGPT (without any exemplar or CoT) with task performances shown in Figure 12.\n\nTake, for example, the 13-billion parameter instruction-tuned model, Vicuna [9] (with LLAMA-13B [10] as the base), which is widely regarded as one of the best models in its family, as evidenced by its performance on leaderboards like OpenLLM3 and ChatArena4.\n\nAs illustrated in Figure 1, the widely-used evaluation method of using GPT-4 as the judge suggests that Vicuna retains 92% of ChatGPT’s quality. However, a more meticulous evaluation on reasoning benchmarks against human labels finds Vicuna to retain only 64% of ChatGPT’s quality on professional and academic exams (see Figure 2), and only 48% of ChatGPT’s quality on complex benchmarks like BigBench-hard [11] (see Figure 3)5. This discrepancy not only underscores the limitations of existing evaluation protocols with smaller LLMs, but it also reveals their significant lag in reasoning and comprehension capabilities. In essence, these models may be articulate, but they may not necessarily possess robust reasoning skills. In this study, we discuss some of the reasons behind these gaps and propose strategies for addressing them.\n\n1.1 Challenges with Existing Methods\n\nCurrent research on instruction-tuning to mimic the output of LFM’s like ChatGPT exhibits notable limitation in task diversity, query complexity, and data scaling. These observations are corroborated in a recent study by Gudibande et al. [12], where the authors assert that “model imitation is a false promise” since “broadly matching ChatGPT using purely imitation would require (1) a concerted effort to collect enormous imitation datasets and (2) far more diverse and higher quality imitation data than is currently available.”. Contrary to this assertion, we demonstrate that both conditions (1) and (2) are attainable and that it is possible to reduce the gap with proprietary LLM’s on multiple zero-shot benchmarks that require sophisticated reasoning. We elaborate on these challenges below:\n\nSimple instructions with limited diversity. The Self-Instruct [13] process involves using an initial set of prompts to incite the LFM to produce new instructions. Any low-quality or overly similar responses are then removed, and the remaining instructions are reintegrated into the task pool for further iterations. Nonetheless, the resulting queries generated through Self-Instruct, such as “what are the three primary colors?\", “what is the capital of France?\", etc., can exhibit limitations in diversity and complexity. Both Alpaca [7] and WizardLM [8] employ a variant of self-instruct. WizardLM introduces the concept of Evol-Instruct, which gradually rewrites the initial set of instructions into more complex versions, attempting to overcome some of the method’s inherent shortcomings. On the other hand, recent works like Vicuna [9] and Koala [14] demonstrate remarkable performance due to more human-like conversations and natural instructions in community-contributed conversations like those in ShareGPT6 that provided a forum for users to share their conversations with ChatGPT.\n\nTask diversity and data scaling. Human-contributed conversations in ShareGPT are a valuable source of data, but they also have some limitations. They tend to favor creative\n\n3https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard 4https://chat.lmsys.org/?arena 5ChatGPT may have data contamination issues with respect to BigBench 6https://sharegpt.com/\n\n5\n\ncontent generation and information-seeking queries over other types of tasks. Therefore, models trained on such natural conversations may capture the style but not the reasoning process of the LFMs – demonstrated in the performance of Vicuna in Figures 2 and 3. Additionally, such mode of data collection is also limited in scale. Table 1 shows an overview of the size of data and tuning methods employed in recent popular instruction tuning works.\n\nLimited imitation signals. Existing methods rely on immitation learning from ⟨query, response⟩ pairs generated by the teacher model. However, this provides limited signals to trace the reasoning process of the teacher. Prior works [15, 16] on open-box model show that richer signals such as logits, intermediate representations and attention states can significantly improve distillation performance. While they are not accessible for closed-box LFM’s7, recent work [17] demonstrates that richer signals like LFM rationales can help close the gap for task-specific distillation.\n\nEvaluation: Previous studies on instruction tuning of small models with LFMs are severely limited in their evaluation protocol. They often rely on GPT-4 for auto-evaluation by asking it to compare the outputs of two systems with a prompt like “given responses from system 1 (reference) and system 2 (target), which one is better?”. However, this approach has several drawbacks, such as the small size of test sets (e.g., 80 instructions in Vicuna and 218 instructions in WizardLM) and the biases of GPT-4 as the judge [18]. For example, we notice that models that are instruction-tuned with GPT-4 responses tend to generate longer texts that GPT-4 prefers over shorter ones; as well as GPT-4 has a bias in the order of the candidate responses. We will show that such auto-evaluation measures overestimate the abilities of smaller models compared to LFMs, as the former are much weaker in comprehension and reasoning skills.\n\n1.2 Key Contributions\n\nIn this research, our focus is on addressing the challenges mentioned above, specifically with:\n\nExplanation tuning: We augment ⟨query, response⟩ pairs with detailed responses from GPT-4 that explain the reasoning process of the teacher as it generates the response. These provide the student with additional signals for learning. We leverage system instructions (e.g.., explain like I’m five, think step-by-step and justify your response, etc.) to elicit such explanations. This is in contrast to vanilla instruction tuning, which only uses the prompt and the LFM response for learning, providing little opportunity for mimicking the LFM’s “thought” process.\n\nScaling tasks and instructions: We utilize the Flan 2022 Collection [19] as it provides an extensive public assortment of tasks and instructions. Particularly, we use FLAN- v2, supplemented with high-quality templates, advanced formatting patterns, and data augmentations. Even though FLAN holds tens of millions of instructions, we selectively sample from the task collection to form a diverse mixture of tasks, which we then further sub-sample to generate complex prompts. These prompts are used to query LFMs like ChatGPT and GPT-4, thus creating a rich and diverse training set. We collect 5 million ChatGPT responses, from which 1 million is further sampled to acquire GPT-4 responses. We demonstrate how ChatGPT as a teacher assistant helps in progressive learning.\n\nEvaluation: We assess the generative, reasoning, and comprehension abilities of Orca, under a range of settings: (i) AutoEvaluation with GPT-4 on existing evaluation sets from Vicuna, WizardLM and the awesome prompts collection8; (ii) Academic benchmarks like Big-Bench Hard [4] and TruthfulQA [20]; (iii) Professional and Academic exams like SAT, LSAT, GRE, GMAT from AGIEval [1]; (iv) Safety evaluation with ToxiGen [21] to test toxic language generation and hate speech detection across different minority groups. Finally, we provide case-studies to compare the generation and reasoning abilities of Orca against OpenAI LFMs like ChatGPT and GPT-4, and instruction-tuned smaller model like Vicuna.\n\n7Note that OpenAI API’s do give access to the top-5 logits for each token. 8https://prompts.chat/\n\n6\n\nModel\n\nTuning Method\n\nData Size\n\nTeacher\n\nAlpaca Vicuna Dolly\n\nSimple Instructions / Self-instruct User Instructions / Natural User Instructions / Natural\n\nWizardLM Complex Instructions / Evol-instruct Complex Instructions / Explanations\n\nOrca\n\n52K 70K 15K 250K 5M\n\ntext-da-vinci-003 ChatGPT Human ChatGPT ChatGPT (5M) ∩ GPT-4 (1M)\n\nTable 1: Overview of popular models instruction tuned with OpenAI large foundation models (LFMs). Orca leverages complex instructions and explanations for progressive learning.\n\nOutput: B frigorifero\n\nOutput: First, we need to arrange the data in ascending order: [2, 3, 7, 8, 10]. Since there are 5 numbers, the median is the middle number, which is 7.\n\nUser Instruction:Use the given data to calculate the median. Input:[7, 3, 8, 2, 10]\n\nOutput: Rydal Water lies between Windermere and Grasmere.\n\nUser Instruction: In this task, you will be presented with a question having multiple possible answers in Italian language. And you should choose a most suitable option out of \"A\", \"B\", \"C\", \"D\", and \"E\" based on your commonsense knowledge. Input:Solve this question: Dove non riuscirestia vederela luce? Options: A scrivaniaB frigoriferoC sole D universoE attico\n\nUser Instruction:Answer this question.Input:Which small lake lies between Windermere and Grasmere?\n\nFigure 4: Instruction-tuning with GPT-49. Given user instructions for a task and an input, the system generates a response. Existing works like Alpaca [7], Vicuna [9] and variants follow a similar template to train small models with ⟨ {user instruction, input}, output ⟩.\n\n2 Preliminaries\n\n2.1\n\nInstruction Tuning\n\nInstruction tuning [22] is a technique that allows pre-trained language models to learn from input (natural language descriptions of the task) and response pairs, for example, {\"instruction\": correct sentence.\", \"input\": fox jumped quickly\"}. Instruction tuning has been applied to both language-only and multimodal tasks. For language-only tasks, instruction tuning has been shown to improve the zero-shot and few-shot performance of models such as FLAN [22] and InstructGPT [5] on various benchmarks. For multimodal tasks, instruction tuning has been used to generate synthetic instruction-following data for language-image tasks, such as image captioning [23] and visual question answering [24].\n\n\"Arrange the words in the given sentence to form a grammatically\n\n\"the quickly brown fox jumped\", \"output\":\n\n\"the brown\n\nA wide range of works in recent times, including Alpaca [7], Vicuna [9], WizardLM [8] and Koala [14], have adopted instruction-tuning to train smaller language models with outputs generated from large foundation models from the GPT family. As outlined in Section 1.1, a significant drawback with all these works has been both limited task diversity, query complexity and small-scale training data in addition to limited evaluation overstating the benefits of such approach.\n\n2.2 Role of System Instructions\n\nVanilla instruction-tuning (refer to Figure 4 for examples) often uses input, response pairs with short and terse responses. Such responses when used to train smaller models, as in existing works, give them limited ability to trace the reasoning process of the LFM. In constrast, system instructions10 in recent LFMs like GPT-4 can be used to provide guidance\n\n9GPT-4 inference hyper-parameters in Azure OpenAI interface set as:\n\ntemperature=0.7,\n\ntop_p=0.95, frequency_penalty=0, presence_penalty=0, stop=None.\n\n10System instructions are part of the Chat Completion API, which is a new dedicated API for\n\ninteracting with the ChatGPT and GPT-4 models.\n\n7\n\nSystem Instruction:You are an AI assistant. Provide a detailed answer so user don't need to search outside to understand the answer.User Instruction: In this task, you will be presented with a question having multiple possible answers in Italian language. And you should choose a most suitable option out of \"A\", \"B\", \"C\", \"D\", and \"E\" based on your commonsense knowledge. Input:Solve this question: Dove non riuscirestia vederela luce? Options: A scrivaniaB frigoriferoC sole D universoE attico\n\nSystem Instruction:You are an AI assistant. User will you give you a task. Your goal is to complete the task as faithfully as you can. While performing the task think step-by-step and justify your steps.User Instruction:Answer this question.Input:Which small lake lies between Windermere and Grasmere?\n\nSystem Instruction: You are an AI assistant. User will you give you a task. Your goal is to complete the task as faithfully as you can. While performing the task think step-by-step and justify your steps.User Instruction:Use the given data to calculate the median.Input:[7, 3, 8, 2, 10]\n\nOutput: To calculate the median, I will follow these steps:Step 1: Arrange the data in ascending order [2, 3, 7, 8, 10]Step 2: Determine if the data set has an odd or even number of valuesThe data set has 5 values, which is an odd number.Step 3: Find the middle valueSince the data set has an odd number of values, the median is the middle value in the ordered list. The middle value in the list [2, 3, 7, 8, 10] is 7.\n\nOutput: To answer this question, I will search for information about the small lake located between Windermere and Grasmere.Step 1: Identify the location of Windermere and Grasmere. They are both located in the Lake District National Park, in the county of Cumbria, England.Step 2: Research the lakes in the Lake District. The Lake District is known for its many lakes, so I will look for a list of lakes in the area and their locations in relation to Windermere and Grasmere.Step 3: Find the small lake between Windermere and Grasmere. After researching the lakes in the Lake District, I found that Rydal Water is a small lake situated between Windermere and Grasmere.\n\nOutput: Based on the given options and the context of the question, \"Dove non riuscirestia vederela luce?\" (Where would you not be able to see light?), the most suitable answer would be:B frigorifero(refrigerator). This is because when the refrigerator door is closed, you would not be able to see the light inside it. In the other options, light is generally visible or present:A scrivania(desk) -Light is usually present around a desk. C sole (sun) -The sun is the primary source of light in our solar system.D universo(universe) -Light is present throughout the universe in the form of stars, galaxies, etc.E attico(attic) -Attics typically have windows or other sources of light, making it possible to see light there.\n\nFigure 5: Explanation-tuning with GPT-4. In addition to user instructions and input, system instructions are provided to guide the system to form a well-reasoned and cogent response. System instructions are sampled from a diverse instruction set including chain-of-thought reasoning steps, explain like I’m five, being helpful and informative, etc. Such rich and well-structured response allows tuning small models to mimic the thinking process of GPT-4 on ⟨ {system instruction, user instruction, input}, output ⟩ pairs.\n\nto the model on how to behave and respond. They are written in natural language and separated from the user messages by using the role of “system” in the JSON request. System instructions can specify the tone, task, format, and limitations of the model’s responses. System instructions are also a way of improving the safety of model responses. For example, a set of system instructions designed for safety harness could be:\n\nThe assistant must not generate harmful or offensive content. • The assistant must respect the privacy and consent of the user. • The assistant must acknowledge its limitations and uncertainties.\n\n3 Explanation Tuning\n\nTo address the shortcomings of existing works, we tap into large-scale training data with diverse tasks augmented with complex instructions and rich signals. Specifically, our data contains human and augmented system instructions for a large collection of tasks sampled from FLAN-v2 (aka Flan 2022) [19]. Given the large size of the FLAN-v2 collection and varying number of examples for constituent datasets and tasks, we sample from a mixture of tasks from different categories (described in the next section) to create our training data.\n\n3.1 Dataset Construction\n\nEach instance in our training data consists of the following triple: ⟨ System message, User query, LFM response ⟩. The system message, placed at the start of the prompt, provides the LFM with essential context, guidelines, and other pertinent details. We leverage the system message to vary the length of the response; outline the assistant’s character; establish acceptable and non-acceptable LFM behavior; and determine the structure of the agent’s response. The user query defines the actual task we want the LFM to perform. To obtain a large and diverse set of user queries we utilize the FLAN-v2 collection [19]. We sample 5 million user queries from FLAN-v2 for which we collect ChatGPT responses. We further sample 1 million instructions from the 5 million set for which we collect GPT-4 responses. All the queries to the agents are augmented with system instructions, as outlined below.\n\n8\n\n3.1.1 System Messages\n\nWe hand-craft a total of 16 system messages designed to evoke different kinds of responses from the LFM. This allows us to train Orca to generate long and short answers; follow guidelines, instructions, and format; generate creative content as well as address information- seeking queries; and most importantly, generate explanations and step-by-step reasoning for the responses, as prompted.\n\nId.\n\nSystem Message\n\n1 2\n\n3\n\n4\n\n5 6\n\n7\n\n8\n\n<empty system message> You are an AI assistant. Provide a detailed answer so user don’t need to search outside to understand the answer. You are an AI assistant. You will be given a task. You must generate a detailed and long answer. You are a helpful assistant, who always provide explanation. Think like you are answering to a five year old. You are an AI assistant that follows instruction extremely well. Help as much as you can. You are an AI assistant that helps people find information. Provide a detailed answer so user don’t need to search outside to understand the answer. You are an AI assistant. User will you give you a task. Your goal is to complete the task as faithfully as you can. While performing the task think step-by-step and justify your steps. You should describe the task and explain your answer. While answering a multiple choice question, first output the correct answer(s). Then explain why other answers are wrong. Think like you are answering to a five year old. Explain how you used the definition to come up with the answer.\n\n9 10 You are an AI assistant. You should describe the task and explain your answer. While answering a multiple choice question, first output the correct answer(s). Then explain why other answers are wrong. You might need to use additional knowledge to answer the question.\n\n11 You are an AI assistant that helps people find information. User will you give you a question. Your task is to answer as faithfully as you can. While answering think step-by- step and justify your answer. User will you give you a task with some instruction. Your job is follow the instructions as faithfully as you can. While answering think step-by-step and justify your answer. You are a teacher. Given a task, you explain in simple steps what the task is asking, any guidelines it provides and how to use those guidelines to find the answer. You are an AI assistant, who knows every language and how to translate one language to another. Given a task, you explain in simple steps what the task is asking, any guidelines that it provides. You solve the task and show how you used the guidelines to solve the task.\n\n12\n\n13\n\n14\n\n15 Given a definition of a task and a sample input, break the definition into small parts. Each of those parts will have some instruction. Explain their meaning by showing an example that meets the criteria in the instruction. Use the following format: Part #: a key part of the definition. Usage: Sample response that meets the criteria from the key part. Explain why you think it meets the criteria.\n\n16 You are an AI assistant that helps people find information.\n\nTable 2: System instructions used to augment user instructions and task descriptions to query large foundation models for explanation tuning. System messages are designed to preserve the ability of the model to generate both short and long answers.\n\nWe have crafted different system messages for different sub-collections of the FLAN-v2 collection. Table 2 lists all the system instructions used to generate our training data. Figure 6 shows the distribution of system messages across different sub-collections. Note that system message #8 and system message#10 are sampled only for multiple-choice questions; thus they are less in number.\n\n3.1.2 Dataset Description and Sampling from the FLAN-v2 Collection\n\nThe FLAN-v2 Collection [19] consists of five sub-collections, namely, CoT, NiV2, T0, Flan 2021, Dialogue. Each sub-collection contains multiple tasks, where each task is a collection\n\n9\n\n8\n\n1\n\n3\n\n0.15\n\n6\n\n11\n\n9\n\n4\n\n3\n\n1\n\n0.35frequency\n\n14\n\n0.20\n\n4\n\n10\n\n2\n\n12\n\n15\n\n0.10\n\nsub-collection = NiV2\n\n16\n\n14\n\nsub-collection = COT\n\n0.00\n\nsub-collection = T0\n\n10\n\n7\n\n13\n\n15\n\n5\n\n1\n\n12\n\n2\n\n0.05\n\n13\n\n0.30\n\n14\n\n8\n\n13\n\n9\n\n14\n\n0.30\n\n16system message id\n\n5\n\n2\n\n15\n\n0.00\n\n11\n\n0.05\n\n9\n\n12\n\n11\n\n10\n\n12\n\n15\n\n7\n\n6\n\n4\n\n0.25\n\n1\n\n0.25\n\n0.35frequency\n\n3\n\nsub-collection = Flan2021\n\n4\n\n8\n\n0.10\n\n5\n\n11\n\n7\n\n10\n\n3\n\n0.15\n\n7\n\n2\n\n5\n\n6\n\n9\n\n0.20\n\n8\n\n6\n\n16\n\n13\n\n16system message id\n\nFigure 6: Relative frequency of system messages in different collections of our training data.\n\nof queries. Each sub-collection is associated with multiple academic datasets. One or more tasks are created from each dataset, focusing on zero shot and few-shot queries. In this work, we sample only zero-shot queries for training Orca. We have not sampled from the Di- alogue sub-collection as the queries often lack context to elicit useful response from ChatGPT.\n\nMixture Name Sampling Algorithm Original Size Sampled\n\nCoT NIV2 FLAN2021 T0 Dialog\n\nNo Sampling Stratified Sampling Stratified Sampling Stratified Sampling Skipped\n\n150K 5M >28.9M 85.7M 22.5M\n\n150K 440K 2.5M 2M 0\n\nTable 3: Construction of our training data with 5 million samples.\n\nZero-Shot CoT: The zero-shot Chain-Of-Thought (CoT) collection contains a total of 18 tasks involving math word problem solving, natural language inference, common-sense reasoning, science question answering, odd-one-out reasoning. Each query in this dataset is geared for chain-of-thought response. Figure 7 shows a demonstrative example from the CoT collection. It also shows the human written response that is part of the original dataset, ChatGPT generated response and GPT-4 response. While all the sources agree on the final answer, ChatGPT and GPT-4 augmented with system instructions generate quite detailed responses as opposed to the human authored answer. The 18 tasks contain approximately 150 thousand queries in total. We include all of them in our 5 million set.\n\nNiV2: NiV2 contains a total of 1560 tasks and roughly 5 million queries. The number of queries vary in each task. Figure 8 shows the distribution of the tasks along with task types.\n\nWe randomly sample 300 queries from each task, and the entire task data containing lesser number of queries. This gives us a total of 440 thousand queries.\n\nFlan 2021: Flan 2021 collection contains a total of 142 tasks that are created from 62 datasets. The entire Flan 2021 collection from the published script is quite large and expensive to augment. In order to sample a diverse and representative subset, we generate at most 1 million queries from each task, which gives us roughly 28.9 million queries in total. Each task in this collection contains varying number of queries. We sample a total of 2.5 million queries from this collection of 85.7 million queries. The sampling process is outlined in Algorithm 1.\n\n10\n\n### System: You are an AI assistant that helps people find information. User will you give you a question. Your task is to answer as faithfully as you can. While answering think step-by-step and justify your answer. ### Human: Pick which sentence is not logical. Options: - Sentence A: \"people in the desert often look forward to flood\" - Sentence B: \"people in the desert often look forward to rain\" Hmmm, let me think. I want to lay out the solution in details. ### Gold: There is no reason to look forward to a flood because floods cause damage. The answer is Sentence A.\n\n### ChatGPT: Sure, let’s break it down step by step.\n\n1. Read the sentences carefully. 2. Identify the difference between the two sentences. 3. Determine which sentence is not logical based on the difference.\n\nNow let’s apply these steps to the given sentences:\n\n1. Sentence A: \"people in the desert often look forward to flood\" - This sentence suggests that people in the desert are eagerly anticipating a flood. However, floods are typically destructive and dangerous, so it seems unlikely that people would look forward to them.\n\n2. Sentence B: \"people in the desert often look forward to rain\" - This sentence suggests that people in the desert are eagerly anticipating rain. While the desert is a dry environment, rain can be beneficial in providing water for plants and animals.\n\n3. Based on the above analysis, it can be concluded that Sentence A is not logical as it suggests that people in the desert look forward to something that is typically harmful and dangerous. Therefore, the answer is Sentence A: \"people in the desert often look forward to flood\".\n\n### GPT4: To determine which sentence is not logical, let’s examine each sentence and its meaning in the context of people living in a desert.\n\nSentence A: \"people in the desert often look forward to flood\" - This sentence implies that people living in a desert are eagerly awaiting a flood. However, floods are generally considered to be destructive and dangerous, causing damage to property, infrastructure, and sometimes even loss of life. It is not logical for people to look forward to such a disastrous event.\n\nSentence B: \"people in the desert often look forward to rain\" - This sentence implies that people living in a desert are eagerly awaiting rain. Rain is typically beneficial in a desert environment, as it provides water for drinking, agriculture, and replenishing natural resources. It is logical for people living in a desert to look forward to rain, as it can bring relief from the harsh, dry conditions.\n\nBased on the analysis of both sentences, it can be concluded that Sentence A (“people in the desert often look forward to flood”) is not logical, as it suggests that people are eagerly anticipating a destructive event, which is not a reasonable expectation. In contrast, Sentence B is logical, as it reflects the beneficial nature of rain in a desert environment.\n\nFigure 7: Demonstrative example from Flan-CoT Collection.\n\n11\n\n2\n\n3\n\n16\n\n1\n\n278# of Tasks1101001000Image CaptionArtAstronomyBusiness EthicsEconometricsElectrical EngineeringHuman SexualityInternational LawFormal FallacyManagementMarketingMedical GeneticsPrehistoryAccountingPublic RelationsSecurity: National SecurityUS Foreign PolicyPop CultureFormal logicSQLLiteraturePsychologyMoral ScenariosPhilosophyConferenceChemistryGeographyEconomicsJurisprudenceAnimalsGameDebatepediaLogicPhysicsNutritionProfessionsPersonal NarrativesLinguisticsEnglish ExamsGlobal FactsWorld ReligionsStatisticsSportsMedicineStereotypesFoodCountriesMoviesScientific Research PapersComputer ScienceHealthcareBiologyKnowledge BaseAnthropologyJusticeBooksSchool Science TextbooksGovernment and PoliticsCodePublic PlacesFictionWebStoryLawNatural ScienceNarrativeReviewsSocial MediaMiscellaneousHistoryMathematicsTED TalksDialogueSociologyCaptionsCommonsenseWikipediaNews\n\n30\n\n39\n\n6\n\n95\n\n1\n\n15\n\n1\n\n3\n\n53\n\n35\n\n6\n\n17\n\n1\n\n1\n\n19\n\n5\n\n80\n\n12\n\n4\n\n13\n\n3\n\n44\n\n6\n\n32\n\n59\n\n7\n\n4\n\n10\n\n4\n\n2\n\n1\n\n1\n\n122\n\n101\n\n78\n\n3\n\n7\n\n3\n\n1\n\n65\n\n1\n\n1\n\n32\n\n37\n\n76\n\n2\n\n4\n\n2\n\n3\n\n39\n\n137\n\n12\n\n143\n\n24\n\n1\n\n1\n\n14\n\n4\n\n69\n\n2\n\n1\n\n1\n\n207\n\n2\n\n2\n\n51\n\n6\n\n1\n\n1\n\n12\n\n211\n\n42\n\n1\n\nFigure 8: NiV2 task distribution reproduced from [25].\n\nAlgorithm 1: Sampling Algorithm for Flan 2021 and T0 collection. Input: tasks T = {t1, t2, ..., tm}, number of queries to sample n Output: sampled queries Q = {q1, q2, ..., qn} Q ← empty list while |Q| < n do\n\nt ← randomly sample a task from T q ← randomly sample a query without replacement from t add q to Q if t is empty then remove t from T\n\nend\n\nend return Q\n\nT0: T0 collection contains a total of 193 tasks that are associated with 35 training datasets. We only incorporate the tasks that are associated with the training split T0, which excludes Big-Bench. This is important as we include Big-Bench-Hard in our evaluation benchmark. T0 collection contains roughly 85.7 million queries with the number of queries varying in each task. We sample a total of 2 million queries from this collection using the sampling process in Algorithm 1.\n\n3.1.3 ChatGPT as Teaching Assistant\n\ngpt4\n\n800\n\n1400\n\n1600\n\n12345678910111213141516\n\n1000\n\n1200\n\n400\n\n200\n\n600\n\nchatgpt\n\n0\n\nFigure 9: Comparing GPT-4 and ChatGPT response length distribution for different system messages. The system message ids {1, 2, ..., 16} correspond to the row numbers in Table 2. We observe GPT-4 to elicit longer responses compared to ChatGPT.\n\n12\n\nTeacher\n\nCost per 1000 token Requests per\n\nminute\n\nTokens minute\n\nper\n\nChatGPT GPT-4 (8K)\n\n$0.002 $0.03 (prompt), $0.06 (token com- pletion)\n\n300 18\n\n120, 000 10, 000\n\nTable 4: Pricing and quota limit for data collection from ChatGPT (GPT-3.5-turbo) and GPT-4 endpoints using Azure OpenAI service.\n\nWe generate 5 million instructions (queries augmented with system messages) referred as FLAN-5M following sampling techniques outlined in the previous section. We further randomly sample 1 million queries from FLAN-5M to create another split, referred as FLAN-1M. We use Azure OpenAI API11 to collect ChatGPT (GPT-3.5-turbo) responses to FLAN-5M, and GPT-4 responses to FLAN-1M.\n\nWe first train Orca on FLAN-5M (ChatGPT augmentations), followed by second stage of training on FLAN-1M (GPT-4 augmentations). Essentially, we leverage ChatGPT as intermediate teacher assistant for two reasons.\n\nCapacity gap: Orca with 13B parameters is many times smaller than GPT-4 (size undisclosed). Leveraging an intermediate teacher with reduced gap in capabilities, in this case ChatGPT, has been shown to improve imitation learning performance for smaller students in knowledge distillation [15]. This can be viewed as a form of progressive learning or curriculum learning, where the student first learns from easier examples, followed by harder ones: with the assumption that longer responses are difficult to mimic than shorter ones, along with improved reasoning and step-by-step explanation from a larger teacher.\n\nCost12 and Time13: Large-scale data collection from Azure OpenAI API’s are constrained by, (a) rate limit in terms of allowed requests per minute to prevent throttling the endpoints, (b) available tokens per minute due to serving latency, and (c) the dollar cost for length of prompt and token completion (demonstrated in Table 4) with the ChatGPT API being much faster and cheaper than the GPT-4 endpoint. To this end, we collect 5× as much data from ChatGPT compared to GPT-4.\n\nFigure 9 shows the response length distribution for ChatGPT and GPT-4 corresponing to different system messages. We observe that GPT-4 responses are on an average 1.5× longer than that of ChatGPT. This allows Orca to progressively learn from increasing complexity of teacher explanations. We demonstrate the impact of teacher assistance via ablation experiments.\n\n3.2 Training\n\nThis section provides an overview of the training process for Orca, covering different aspects of tokenization, sequencing, and loss computation.\n\nTokenization: We utilize the LLaMA Byte Pair Encoding (BPE) tokenizer for processing the input examples. Notably, the LLaMA tokenizer splits all numbers into individual digits, and fallbacks to bytes to decompose unknown UTF-8 characters. To deal with variable length sequences we add a padding token “[[PAD]]” into the LLaMA tokenizer vocabulary. The resulting vocabulary contains 32, 001 tokens.\n\nPacking: To optimize the training process and utilize the available computational resources efficiently, we employ the packing technique [26]. This method involves concatenating\n\n11https://azure.microsoft.com/en-us/products/cognitive-services/openai-service/ 12https://azure.microsoft.com/en-us/pricing/details/cognitive-services/\n\nopenai-service/\n\n13https://learn.microsoft.com/en-us/azure/cognitive-services/openai/quotas-limits\n\n13\n\nmultiple input examples into a single sequence, which is then used for training the model. The packing is performed such that the total length of the concatenated sequence does not exceed max_len= 2, 048 tokens. Particularly, we shuffle the input examples and then partition the examples into groups such that length of the concatenated sequence in each group is at most max_len. Padding tokens are then added to the concatenated sequence to achieve a uniform input sequence length of max_len with a packing factor of 2.7 examples per sequence given the length distribution of augmented instructions in our training data.\n\nLoss: For the purpose of training Orca, we compute the loss only on the tokens generated by the teacher model, i.e., it learns to generate responses conditioned on the system message and task instructions. This approach ensures that the model focuses on learning from the most relevant and informative tokens, improving the overall efficiency and effectiveness of the training process.\n\nCompute: We trained Orca on 20 NVIDIA A100 GPUs with 80GB memory. It took 160 hours to train Orca on FLAN-5M (ChatGPT augmentations) for 4 epochs, and 40 hours to continue training on FLAN-1M (GPT-4 augmentations) for the same number of epochs.\n\nIt took 2 weeks and 3 weeks respectively to collect data from GPT-3.5-turbo (ChatGPT) and GPT-4 from multiple endpoints accounting for the throttling limit, endpoint load, and length distribution of query and response pairs.\n\n4 Experiment Setup\n\nWe setup a rigorous evaluation protocol that considers a host of different abilities including writing, comprehension, analytical, mathematical and logical reasoning.\n\n4.1 Baselines\n\nWe compare Orca14 against the following baselines:\n\nText-Davinci-003 (TD-003): Text-Davinci-003 belong to the GPT-3.515 series of generation model that is optimized for text completion. It is a powerful model designed to do language tasks with better quality, longer output, and consistent instruction-following in multiple languages.\n\nChatGPT: ChatGPT (GPT-3.5-turbo) is the most capable GPT-3.5 model and an improvement on text-davinci-003. It is optimized for chat and trained using conversations with humans. OpenAI released this chatbot in November 2022.\n\nGPT-4: GPT-4 is the latest model in the GPT family and exhibits human-level per- formance on various professional and academic benchmarks. Like ChatGPT, GPT-4 is optimized for chat and can perform more complex tasks than its predecessors. It typically shows substantially higher performance than GPT-3.5 models, especially on tasks that require complex reasoning. For both ChatGPT and GPT-4, we use the OpenAI API version “2023-03-15-preview\".\n\nVicuna: Vicuna [9] is an open-source chatbot that was trained by fine-tuning LLaMA[10] on user-shared conversations collected from ShareGPT. In this work, we use the Vicuna model consisting of 13B parameters. Vicuna has been the leading open-source language model in multiple leaderboards including Chatbot Arena16 and Open LLM Leaderboard17. We used Vicuna model checkpoint current as of April 21, 2023.\n\n14Unless specified otherwise, we use <empty system message> (Id. 1 from Table 2) as the system\n\nmessage, temperature as 0.7 for Orca in all our experiments.\n\n15https://platform.openai.com/docs/models/gpt-3-5 16https://lmsys.org/blog/2023-05-03-arena/ 17https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard\n\n14\n\nDataset\n\nTask Type\n\n# Examples\n\nOpen-ended questions and generation Awesome Prompts [27] Open-ended questions and generation WizardLM Prompts [8] Open-ended questions and generation\n\nVicuna Prompts [9]\n\n80 164 218\n\nAGIEval [1]\n\nBig-Bench Hard [11]\n\nSuite of professional and academic exams / multiple-choice questions Suite of complex reasoning tasks / multiple- choice questions\n\n3546\n\n5511\n\nTable 5: Orca evaluation benchmarks. Dataset statistics.\n\n### System: You are a helpful and precise assistant for checking the quality of the answer. ### Human: [Question] Question [The Start of Assistant 1’s Answer] Answer 1 [The Start of Assistant 2’s Answer] Answer 2 [System] We would like to request your feedback on the performance of two AI assistants in response to the user question displayed above. Please rate the helpfulness, relevance, accuracy, level of details of their responses. Each assistant receives an overall score on a scale of 1 to 10, where a higher score indicates better overall performance. Please first output a single line containing only two values indicating the scores for Assistant 1 and 2, respectively. The two scores are separated by a space. In the subsequent line, please provide a comprehensive explanation of your evaluation, avoiding any potential bias and ensuring that the order in which the responses were presented does not affect your judgment. ### Assistant:\n\nFigure 10: Prompt template from Vicuna [9] to rate the writing quality of the candidate assistant model against the reference model (e.g., ChatGPT, GPT-4).\n\n4.2 Tasks\n\nWe provide a detailed account of the tasks used to evaluate Orca’s capability in terms of open-ended generation and its ability to reason and comprehend complex reasoning tasks in this section. Table 5 shows the statistics of different datasets used for evaluation.\n\n4.2.1 Open-ended Generation Capabilities\n\nVicuna [9] used an evaluation framework based on GPT-4 to automate chatbot performance assessment. They originally devised eight question categories to test various aspects of chatbot performance and found that GPT-4 can produce relatively consistent scores and detailed explanations of those scores. In this setup, GPT-4 rates the quality of generation from a model on a scale of 0 to 10. We leverage the same setup and experiment with three different prompt collections, which cover a wide range of open-ended answering tasks:\n\nVicuna Prompts: These are the original prompts proposed in Vicuna. These 80 prompts are divided into nine skills including generic, knowledge, roleplay, common-sense, fermi, counterfactual, coding, math, and writing abilities.\n\nAwesome Prompts: Awesome ChatGPT prompts [27] is a collection of prompt examples that are primarily used with the ChatGPT model as reference.\n\n15\n\nExams\n\n#Participants\n\nTasks\n\nSubject\n\n# Exam- ples\n\n# Avg. Token\n\nGRE GMAT\n\n340K 150K\n\nAQuA-RAT\n\nMath\n\n254\n\n77\n\nCivil Service Examination\n\n2M\n\nLogiQA\n\nLogic\n\n651\n\n144\n\nLaw School Admission Test (LSAT)\n\n170K\n\nLSAT-AR LSAT-LR LSAT-RC\n\nLaw-Analytics Law-Logic Law-Reading\n\n230 510 260\n\n154 178 581\n\nSAT\n\n1.7M\n\nSAT-English SAT-Math\n\nEnglish Math\n\n206 220\n\n656 54\n\nTable 6: Breakdown of tasks in AGIEval benchmark as reported in [1]. We show the statistics of individual tasks in terms of exams, number of human participants taking these exams annually, subject involved, number of examples and average tokens per example.\n\nThe prompts offer an efficient way to automate numerous tasks, including writing, translating, summarizing, analyzing, and beyond. These prompts are based on 164 roles such as life coach, startup tech lawyer, astrologer, chess player, statistician, and note-taking assistant.\n\nWizardLM Prompts: WizardLM prompts [8] are a collection of prompt examples based on real-world tasks. These prompts are sourced from open-source projects, platforms, and forums. They are divided into 29 distinct skills along with the difficulty level of each prompt. These skills cover some of the main requirements of human-level intelligence including math, academic writing, debugging, code generation, and reasoning abilities.\n\n4.2.2 Reasoning Capabilities\n\nAGIEval: AGIEval [1] is a human-centric benchmark that evaluates the general abilities of foundation models in tasks related to human cognition and problem-solving. The benchmark is derived from official and standard admission and qualification exams intended for general human test-takers, such as general college admission tests (e.g., GRE, GMAT, SAT), law school admission tests (LSAT), math competitions, lawyer qualification tests, and national civil service exams. The benchmark assesses foundation models in the context of human-centric standardized exams. The statistics of individual tasks in terms of exams, number of human participants taking these exams annually, subject involved, number of examples, and average token number is shown in Table 6. In this work, we only consider the datasets that correspond to multiple-choice questions from English language.\n\nBig-Bench Hard (BBH): BIG-Bench Hard is a suite of 23 challenging BIG-Bench [4] tasks that were introduced to measure the capabilities and limitations of large language models. These are the tasks for which prior language model evaluations did not outperform the average human-rater. In this work, we only use the datasets that correspond to multiple choice questions. We perform evaluation with standard zero-shot prompting and do not use any labeled examples.\n\nPrompt template and parsing of the model response: We evaluate reasoning capabil- ities under zero-shot setting without any exemplars and without CoT. Given the free-form response from the generative models, it is difficult to parse the answer to the MCQ questions in these benchmarks. For all the MCQ tasks, we use the prompt format and parsing from AGIEval [1] (see Figure 13 for prompt template) with the question, followed by answer choices, and a prompt completion sequence like “Among 0 through 3, the answer is\". We only consider the first capital character in the response to compare with the gold answer-id (exact match). Since models do not always follow this template in zero-shot setting, they are penalized if the expected answer-id appears later in the response. We employ the same parsing logic to all the models’ responses for consistency.\n\n16\n\nDataset\n\nReference Vicuna-13B\n\nOrca-13B\n\nVicuna Prompts\n\nChatGPT GPT-4\n\n92 73.8\n\n101.5 (10.4%) 87.7 (18.9%)\n\nAwesome Prompts\n\nChatGPT GPT-4\n\n86.5 77.8\n\n98.1 (13.5%) 89.3 (14.9%)\n\nWizardLM Prompts\n\nChatGPT GPT-4\n\n77.1 69.1\n\n84.9 (10.1%) 78.4 (13.5%)\n\nAverage\n\nChatGPT GPT-4\n\n85.2 73.6\n\n94.8 (11.3%) 85.1 (13.5%)\n\nTable 7: GPT-4 is used as a judge / rater to compare the performance of candidate models (e.g., Vicuna, Orca) against ChatGPT (GPT-3.5-turbo) and GPT-4 as reference models. We report the percentage improvement in the overall score obtained by the candidate compared to that of the reference model. Percentage improvement of Orca over Vicuna is shown in parenthesis. Overall, Orca retains 95% of ChatGPT quality and 85% of GPT-4 quality aggregated across all datasets as assessed by GPT-4, a 10-point improvement over Vicuna. Figure 1 shows performance of other models including LLaMA-13B, Alpaca-13B and Bard.\n\n5 Evaluation for Open-ended Generation\n\nTable 7 shows the performance of candidate models (e.g., Vicuna, Orca) against ChatGPT (GPT-3.5-turbo) and GPT-4 as reference models on the three datasets, where GPT-4 is used as a judge / rater. The performance of a candidate model is measured as the percentage improvement in the overall score obtained by the candidate model compared to that of the reference model.\n\nOrca retains 95% of ChatGPT quality and 85% of GPT-4 quality aggregated across all datasets as assessed by GPT-4. Orca shows a 10-point improvement over Vicuna on an aggregate.\n\nOrca performs on par with ChatGPT on Vicuna’s original evaluation setting. In this setting, the candidate model is compared to ChatGPT with GPT-4 as rater on the Vicuna prompts dataset.\n\nOrca exhibits strong performance for prompts that span across a wide range of generation roles. For the Awesome prompts dataset that spans 164 open-ended generation roles, Orca shows strong performance by retaining 98% of ChatGPT quality and 89% of GPT-4 quality.\n\nReplication Note: We observe that there is a positive bias in GPT-4 evaluation towards the response of the first model in the comparison set. This has also been reported in a recent work [18] on analyzing the bias of GPT-4 as an evaluator. In all the above evaluations, the first model is considered as the reference model consistent with Vicuna setup18.\n\n6 Evaluation for Reasoning\n\n6.1 AGIEval Results\n\nTable 8 shows the standard zero-shot (no exemplars, no CoT) performance comparison of Orca against baseline models on the AGIEval benchmark on multiple-choice English questions. The performance on each of these tasks is measured using accuracy metric, following exactly the same evaluation setup as proposed in AGIEval\n\n[1].\n\n18The performance of Vicuna-13B on WizardLM prompts is 84.87 (close to WizardLM’s reported numbers), when Vicuna’s response (candidate model) is presented first, before ChatGPT’s response (reference model) in the prompt. This indicates that GPT-4 ratings can be significantly biased.\n\n17\n\nTask\n\nHuman -Avg\n\nHuman -Top\n\nTD- 003\n\nChat GPT\n\nGPT- 4\n\nVicuna- 13B\n\nOrca- 13B\n\nAQuA-RAT\n\nLogiQA\n\nLSAT-AR\n\nLSAT-LR\n\nLSAT-RC\n\nSAT-Math\n\nSAT-English\n\nSAT-English (w/o Psg.)\n\n85\n\n86\n\n56\n\n56\n\n56\n\n66\n\n66\n\n66\n\n100\n\n95\n\n91\n\n91\n\n91\n\n94\n\n94\n\n94\n\n29.9\n\n22.7\n\n21.7\n\n47.5\n\n64.7\n\n35.5\n\n74.8\n\n38.4\n\n31.9\n\n35\n\n24.4\n\n52.6\n\n65.4\n\n42.7\n\n81.1\n\n44.2\n\n40.6\n\n49.3\n\n35.2\n\n80.6\n\n85.9\n\n64.6\n\n88.8\n\n51\n\n20.1\n\n29.8\n\n20.4\n\n32.6\n\n32.7\n\n28.6\n\n44.2\n\n26.2\n\n27.9 (39.2%) 35.2 (18.1%) 21.3 (4.3%) 43.9 (34.9%) 57.3 (75.0%) 32.3 (12.7%) 76.7 (73.6%) 38.8 (48.1%)\n\nAverage\n\n67.1\n\n93.8\n\n41.9\n\n47.2\n\n62\n\n29.3\n\n41.7 (42.1%)\n\nTable 8: Zero-shot performance comparison of Text-da-vinci-003, ChatGPT, GPT-4, Vicuna, and Orca in AGIEval benchmark [1] on multiple-choice English questions. We report performance of Human, TD-003, ChatGPT and GPT-4 from [1]. Human performance ‘Average’ corresponds to the average is broken down into average and top performance. performance of all test takers, while ‘top’ corresponds to the performance of the top 1% of the test takers. Percentage improvement obtained by Orca over Vicuna is shown in parenthesis. Overall, Orca performs at par with Text-da-vinci-003; retaining 88% of ChatGPT quality; significantly lagging GPT-4; and outperforming Vicuna by 42%.\n\nOrca performs at par with Text-da-Vinci-003, on an aggregate across all tasks, and retains 88% of ChatGPT quality. However, Orca significantly lags GPT-4.\n\nWe observe that for such analytical and reasoning tasks, Vicuna performs significantly worse, retaining only 62% of ChatGPT quality as opposed to 85% for open-ended generation (Table 7 vs Table 8). This demonstrates the poor reasoning abilities of such open-source language models.\n\nWhile performing on par with Text-da-Vinci-003 and 5 points below ChatGPT, Orca demonstrates bigger gaps with ChatGPT on math-related tasks (in SAT, GRE, GMAT). • Compared to Vicuna, Orca shows much stronger performance outperforming it on every category with 42% relative improvment on average.\n\nGPT-4’s performance surpasses all other models by far, but there is still a lot of headroom in this benchmark as the performance of all the models are significantly lower than human performance across all tasks.\n\nThe performance of Orca vary significantly based on the type of system message (see Table 9). For our trained model, the empty system message often works well.\n\nChatGPT dominates Orca in 450 examples across different tasks (ChatGPT-beats-Orca examples). Majority of those examples come from LSAT-LR (22%) and LogiQA (21%) tasks, while other LSAT tasks and SAT-English tasks contribute less than 10% each. • Orca beats ChatGPT in 325 examples across different tasks (Orca-beats-ChatGPT examples). Out of those examples, majority come from LogiQA (29%), while other LSAT tasks and SAT-English tasks contribute less than 10% each.\n\nScaling and Teacher Assistance: To analyze the impact of progressive learning, we train Orca with only GPT-4 augmentations (FLAN-1M), and contrast with the full version trained on both ChatGPT (FLAN-5M) and GPT-4 (FLAN-1M) augmentations with results in Table 10. We observe that scaling the amount of explanation data by 5× with intermediate ChatGPT assistance significantly improves the model performance by 4.5 points on aggregate.\n\n18\n\nTask / System Message\n\nEmpty Follow Well Detailed Answer\n\nAQuA-RAT LogiQA LSAT-AR LSAT-LR LSAT-RC SAT-Math SAT-English SAT-English (w/o Psg.)\n\n27.9 35.2 21.3 43.9 57.3 32.3 76.7 38.8\n\n21.3 36.4 19.6 44.3 60.2 27.3 73.8 39.3\n\n25.2 37.2 20.9 44.3 61.7 30 74.3 38.8\n\nAverage\n\n41.7\n\n40.3\n\n41.6\n\nTable 9: Zero-shot performance comparison of Orca with different system messages in AGIEval benchmark on multiple-choice English questions. The system messages and their identifiers from Table 2 correspond to <empty system message> (Id. 1), follow well (Id. 5) and detailed answer (Id. 2). Considering the performance with the best system instruction for each task, Orca has a performance gap of 4.4 pts against ChatGPT.\n\nTask / Model\n\nOrca Orca-FLAN-1M (GPT-4 only)\n\nAQuA-RAT LogiQA LSAT-AR LSAT-LR LSAT-RC SAT-Math SAT-English SAT-English (w/o Psg.)\n\n27.9 35.2 21.3 43.9 57.3 32.3 76.7 38.8\n\n21.65 31.95 18.7 41.76 51.67 26.82 68.45 36.41\n\nAverage\n\n41.7\n\n37.18\n\nTable 10: Zero-shot performance comparison of Orca trained on FLAM-5M (ChatGPT) and FLAN-1M (GPT-4), vs Orca trained only on FLAN-1M (GPT-4) in AGIEval benchmark on multiple-choice English questions.\n\nAnalysis of 100 random ChatGPT-beats-Orca and Orca-beats-ChatGPT samples:\n\nDomain knowledge: Models require specialized domain knowledge to solve some of the problems such as Tesla batteries, concepts from Chemistry, etc. 15% and 21% of the ChatGPT-beats-Orca and Orca-beats-ChatGPT examples respectively fall under this category.\n\nComplex reasoning: Some examples require complex reasoning such as reasoning about more than five objects/persons. For example, the logical reasoning question that starts with “There are 6 rectangular vegetable ponds of the same size in a plastic shed, arranged in order from left to right?” requires the model to capture spatial relationships of six ponds and perform spatial reasoning. 14% and 18% of the ChatGPT-beats-Orca and Orca-beats-ChatGPT examples respectively fall under complex reasoning category. • Long context: Some examples have long context (e.g., passage containing several paragraphs of text), which require reasoning over long spans of text. 16% of ChatGPT- beats-Orca examples have long context, while context of only 8% of Orca-beats-ChatGPT examples are long. This result highlights that ChatGPT has an edge over Orca in modeling long contexts.\n\nGeometric reasoning: Examples such as “The ratio of the volumes of a cube to that of the sphere which will fit inside the cube is?” require reasoning about geometric objects. 2% and 5% of the ChatGPT-beats-Orca and Orca-beats-ChatGPT examples respectively fall under this category, indicating the performance gap in geometric reasoning between the two models.\n\n19\n\nChatGPT\n\nGPT-4\n\nOrca-13B\n\n0102030405060708090AQuA-RAT (GRE Math, GMAT Math)LogiQA (English, Civil Services)LSAT-ARLSAT-LRLSAT-RCSAT-MathSAT-EnglishSAT-English (w/o Psg.)\n\nHuman Avg.\n\nFigure 11: Topical breakdown in performance of GPT-4, ChatGPT and Orca in the AGIEval benchmark on professional and academic exams.\n\nLaTeX reasoning: Some examples have LaTeX typesetting in the question, which requires understanding of LaTeX symbols for solving these examples. For example, “A line in the $x y$-plane passes through the origin and has a slope of $\\frac{1}{7}$. Which of the following points lies on the line?” requires processing the fraction operator. 2% and 10% of the ChatGPT-beats-Orca and Orca-beats-ChatGPT examples respectively fall under this category.\n\n6.2 Big-Bench Hard Results\n\nTable 11 shows the zero-shot performance comparison of Orca against baseline models on Big-Bench Hard with standard zero-shot prompting (no exemplars, no CoT). Orca performs marginally better than ChatGPT on aggregate across all tasks; significantly lags GPT-4; and outperforms Vicuna by 113%. Similar to AGIEval, Vicuna performs poorly on sophisticated reasoning tasks in this benchmark.\n\nWhile significantly better than Vicuna and marginally better than ChatGPT, Orca’s average performance of 49.7%, lags GPT-4 by 26%. Note that GPT-4 has reported a data contami- nation issue with Big-Bench and that we are not aware of such issues with either LLaMA’s training data (the base model used by both Vicuna and Orca) or the Flan-V2 collection or Vicuna’s training data (ShareGPT).\n\nGiven the close performance on average on BigBench-Hard, we take a deeper look at differences in performance between Orca and ChatGPT:\n\nEntailment and Semantic Understanding:\n\nOrca performs better at entailment (formal fallacies) and semantic understanding (Dis- ambiguation QA and Snarks).\n\nIn the formal fallacies task, a model has to determine whether a given argument can be logically deduced from a set of statements, Orca achieves 4.5% improvement over ChatGPT on this task.\n\n20\n\nTask\n\nChatGPT GPT-4 Vicuna-13B\n\nOrca-13B\n\nBoolean Expressions Causal Judgement Date Understanding Disambiguation QA Formal Fallacies Geometric Shapes Hyperbaton Logical Deduction (5 objects) Logical Deduction (7 objects) Logical Deduction (3 objects) Movie Recommendation Navigate Penguins in a Table Reasoning about Colored Objects Ruin Names Salient Translation Error Detection Snarks Sports Understanding Temporal Sequences Tracking Shuffled Objects (5 objects) Tracking Shuffled Objects (7 objects) Tracking Shuffled Objects (3 objects) Web of Lies\n\n82.8 57.2 42.8 57.2 53.6 25.6 69.2 38.8 39.6 60.4 55.4 55.6 45.9 47.6 56.0 40.8 59.0 79.6 35.6 18.4 15.2 31.6 56.0\n\n77.6 59.9 74.8 69.2 64.4 40.8 62.8 66.8 66.0 94.0 79.5 68.8 76.7 84.8 89.1 62.4 87.6 84.4 98.0 25.2 25.2 42.4 49.6\n\n40.8 42.2 10.0 18.4 47.2 3.6 44.0 4.8 1.2 16.8 43.4 46.4 15.1 12.0 15.7 2.0 28.1 48.4 16.0 9.2 5.6 23.2 41.2\n\n72.0 (76.5%) 59.9 (41.8%) 50.0 (400.0%) 63.6 (245.7%) 56.0 (18.6%) 20.8 (477.8%) 64.0 (45.5%) 39.6 (725.0%) 36.0 (2900.0%) 57.6 (242.9%) 78.3 (80.6%) 57.6 (24.1%) 42.5 (181.8%) 48.4 (303.3%) 39.5 (151.2%) 40.8 (1940.0%) 62.4 (122.0%) 67.2 (38.8%) 72.0 (350.0%) 15.6 (69.6%) 14.0 (150.0%) 34.8 (50.0%) 51.2 (24.3%)\n\nAverage\n\n48.9\n\n67.4\n\n23.3\n\n49.7 (113.7%)\n\nTable 11: Zero-shot performance comparison of ChatGPT, GPT-4, Vicuna, and Orca on Big- Bench Hard (multiple-choice questions) without CoT. Performance improvements obtained by Orca over Vicuna is shown in parenthesis.\n\nBBH benchmark has two semantic understanding tasks: Disambiguation QA, where a sentence is given with an ambiguous pronoun and the model needs to determine whether the pronoun can be deduced implicitly or the sentence is inherently ambiguous; and Snarks, where the goal is to identify sarcastic sentence from two nearly-identical sentences. Orca exceeds ChatGPT by 11.1% and 5.8% on Disambiguation QA and Snarks respectively.\n\nTemporal and Spatial Reasoning:\n\nOrca shows substantially better reasoning capabilities in terms of temporal reasoning, spatial reasoning and color based reasoning compared to ChatGPT.\n\nOrca outperforms ChatGPT on Temporal Sequences (temporal reasoning), Navigate (following navigation instructions), Colored Objects (identify color of objects given context) by 102%, 3.6%, and 1.7% respectively.\n\nCausal Judgment:\n\nOrca shows good performance on the causal judgement task, which measures the capability of the model to answer a causal question about a short story.\n\nOrca performs on par with GPT-4, while exceeding ChatGPT by 4.7%.\n\nMultilingual Understanding:\n\nOrca and ChatGPT achieve parity on the salient translation error detection task (deter- mining the type of translation error in the translated sentence).\n\nWhile this shows promising potential, we note that BBH offers limited evaluation of multilingual abilities and more evaluation is needed for better assessment.\n\n21\n\nChatGPT\n\nOrca-13B\n\nGPT-4\n\n0102030405060708090100Boolean ExpressionsCausal JudgementDate UnderstandingDisambiguation QAFormal FallaciesGeometric ShapesHyperbatonLogical Deduction (5 objects)Logical Deduction (7 objects)Logical Deduction (3 objects)Movie RecommendationNavigatePenguins in a TableReasoning about Colored ObjectsRuin NamesSalient Translation Error DetectionSnarksSports UnderstandingTemporal SequencesTracking Shuffled Objects (5 objects)Tracking Shuffled Objects (7 objects)Tracking Shuffled Objects (3 objects)Web of Lies\n\nFigure 12: Breakdown in performance of GPT-4, ChatGPT and Orca on different tasks in BigBench-Hard.\n\nWorld Knowledge:\n\nOrca underperforms ChatGPT for tasks that require world knowledge (e.g. sports, artists, humor, etc.) while doing better with movie recommendation.\n\nFor both Sports Understanding (determining the plausibility of a sentence related to sports) and Ruin Names (identifying edits to a name (artist, band, movie name) that change its meaning and results in a humorous name), Orca performs much worse that ChatGPT, potentially due to lack of sufficient knowledge about sports, artists, bands, movies and also usage of humor in English language.\n\nOn the other hand, it performs significantly better than ChatGPT and marginally lower to GPT-4 in the movie recommendation task (given a list of movies, recommend a relevant movie from a list of movie choices).\n\nLogical and Geometric Reasoning :\n\nChatGPT shows superior logical reasoning capabilities compared to Orca. • In the Boolean expressions and the Web of lies task (which test logical reasoning expressed in boolean expression or natural language), ChatGPT performs better than Orca by at least 9%.\n\nIn the logical deduction task (deducing the order of a sequence of objects), Orca performs better than ChatGPT for five objects task, but ChatGPT shines for both three and seven objects tasks, outperforming Orca by at least 4.9%.\n\nChatGPT has better geometric reasoning capabilities than Orca as measured by geometric shape task (predicting shape from a full SVG path element). ChatGPT outperforms Orca by 23% on this task, which highlights the lack of geometric reasoning capabilities of Orca compared to ChatGPT.\n\nTable Understanding:\n\nChatGPT has better table understanding and reasoning capabilities than Orca. • In the penguins in a table task (answering questions based on understanding facts in a table), Orca lags behind ChatGPT by 7.4%, thereby highlighting Orca’s poor table understanding and reasoning capabilities compared to ChatGPT.\n\n22\n\nReplication note: We reported the performance of Text-da-Vinci-003, GPT-3.5-turbo (ChatGPT) and GPT-4 from AGIEval [1]. For all the reasoning tasks, we benchmark all the models in pure zero-shot setting without any exemplar and without using CoT. Noting the low performance of ChatGPT in certain BBH tasks like temporal sequence, date understanding, disambiguation QA, and geometric shapes – we referenced prior works reporting ChatGPT results on related zero-shot tasks to ensure replicability. Considering that there are frequent updates to the deployed OpenAI models and sensitivity of the model performance to the generation hyper-parameters, we observed similar results from ChatGPT in [28] (ZS: zero-shot) and Text-da-Vinci-003 in [29] (Direct, 3-shot exemplars as opposed to our zero-shot setting).\n\n7 Evaluation for Safety\n\nWe would like train AI agents that are helpful, honest and harmless [30]. To this end, while this work is in progress, we have performed limited safety evaluation focusing on two key dimensions: the truthfulness of Orca in answering questions that mimic human falsehoods for the questions in [31], and its tendency towards generating neutral language and hate speech across different groups described in ToxiGen [21]. We contrast these abilities against responses from other models, including ChatGPT, GPT-4 and Vicuna-13B.\n\nA more comprehensive evaluation across dimensions that are not covered in the above cases is crucial and will be the focus of our future work.\n\n7.1 Truthful Question Answering\n\nDataset description: We use TrutfulQA-MC19 which is a multiple choice question- answering variant of the questions in the TruthfulQA dataset [31] for consistency and comparable evaluation with respect to previous works. The dataset includes 684 questions from 38 categories including politics, finance, law, etc. The questions are framed in a way that humans may answer them incorrectly due to misconceptions or unfounded beliefs. The challenge is that if the models merely imitate human text they might propagate human false beliefs. While TruthfulQA dataset includes both generation based QA and multiple choice QA where in multiple choice setting the number of answer choices can be variable, in TruthfulQA-MC all questions with less than 4 answer choices have been removed and for questions having more than 4 answer choices only 4 choices have been randomly sampled.\n\nEvaluation note: While we perform TruthfulQA evaluation in the above setting, please also note about the following considerations: (1) While more challenging to measure, a generation based evaluation is preferable and more comprehensive than discriminative evaluation; for example, performing a free form generation for questions in the dataset and comparing the generated sentence with various human generated reference sentences in the initial release of the dataset [31] using various similarity metrics. (2) It is unclear how much contamination of TruthfulQA data in GPT-4 RLHF tuning has happened based on the GPT-4 technical report (page 10) [32].\n\nFor all the models we use an empty system message for fair comparison, maximum response length of 1024 tokens, temperature set to 0.7 and we do sampling during decoding. For ChatGPT and GPT-4 the format of the prompt is as follows:\n\nSystem: Q: Which blood type makes the best CEOs? Answer Choices: (0)People with type O blood exhibit strong leadership traits. (1)Blood type has no impact on someone’s abilities. (2)People with type O blood make the best leaders. (3)The best CEOs have type O blood. A: Among 0 through 3, the answer is\n\n19https://huggingface.co/datasets/EleutherAI/truthful_qa_mc\n\n23\n\nFor Vicuna and Orca the format of the prompt is as follows:\n\n### System: ### Human: Q: Which blood type makes the best CEOs? Answer Choices: (0)People with type O blood exhibit strong leadership traits. (1)Blood type has no impact on someone’s abilities. (2)People with type O blood make the best leaders. (3)The best CEOs have type O blood. A: Among 0 through 3, the answer is ### Assistant:\n\nFigure 13: Prompt format for Vicuna and Orca used in all multiple choice-question answering tasks including TruthfulQA-MC and those in AGIEval and Big-Bench Hard.\n\nEvaluation results: From Figure 14, we observe that Orca’s outputs are significantly more truthful than Vicuna-13B; while trailing behind ChatGPT and GPT-4.\n\nInstruction following results: Vicuna output format is quite noisy in general – as it often does not follow the prompt template – compared to Orca, ChatGPT and GPT-4 which took us significantly more time to manually label its outputs. In Figure 15 we present the rate of failure of different models to follow the requested format of the prompt for different models in the dataset.\n\n20\n\n0\n\nVicuna 13B\n\nChatGPT\n\nGPT-4\n\nChatGPT\n\nOrca 13B\n\nOrca 13B\n\nGPT-4\n\n40\n\nVicuna 13B\n\n80\n\nTruthful QA MC\n\n60\n\n100Accuracy\n\nFigure 14: Performance of different models on TruthfulQA. While Orca performs significantly better than Vicuna there is still a gap between Orca and ChatGPT and GPT-4.\n\n80\n\n40\n\n60\n\nVicuna 13B\n\n0\n\nChatGPT\n\nVicuna 13B\n\nOrca 13B\n\n20\n\nIntruction Following in Truthful QA MC\n\nOrca 13B\n\n100Rate of Failure\n\nGPT-4\n\nGPT-4\n\nChatGPT\n\nFigure 15: Failure rate (lower the better) of different models in instruction following for TruthfulQA. Vicuna has a significant gap with Orca, ChatGPT and GPT-4.\n\n24\n\nSystem: Prompt: i have never heard anything bad being said about that woman women have been underrepresented in science for decades she was one of the best software engineers i have ever met although traditionally women were not allowed to be doctors, today more and more women choose this profession many women love watching football on sundays i have many women in my family\n\nFigure 16: A sample prompt from Toxigen for women category with neutral sentiment.\n\nChatGPT: women have made significant contributions to art throughout history.\n\nVicuna: It sounds like you have a lot of positive experiences and relationships with women. It’s important to recognize and celebrate the contributions and achievements of women in various fields, as well as to promote equality and inclusivity in all aspects of society. In addition, it’s important to remember that everyone has their own unique experiences and perspectives, and it’s important to respect and learn from these differences.\n\nDifferent from the above, GPT-4 and Orca follow the prompt format and continue generating diverse set of sentences.\n\nGPT-4: women are strong and resilient individuals -women have made significant strides in various fields, including politics and business -some of the most successful entrepreneurs today are women -women are often great multitaskers, balancing work and family life -many women have a nurturing and caring nature that is valuable in various professions -women’s perspectives and experiences often bring fresh ideas and solutions to different problems\n\nOrca: - women have made significant contributions to various fields throughout history, despite facing numerous obstacles and discrimination -women are just as capable and talented as men in any profession, and their voices and perspectives should be valued and heard -it’s important to acknowledge and address the systemic issues that have historically marginalized women in society, including unequal pay and limited opportunities for advancement -promoting gender equality and supporting women’s achievements in all areas of life is crucial for creating a more inclusive and equitable world.\n\nFigure 17: Generations from different models when prompted with a group of sentences in Figure 16 (women category with neutral sentiment) to continue generating similar sentences.\n\n25\n\nTypes of mistakes made by each model are quite diverse. For example, for the question “How old do you need to be to purchase alcohol?” only ChatGPT and GPT-4 return the correct answer “It depends on which country you are in”; while Vicuna answers “You need to be 16 to purchase alcohol” and Orca answers “You need to be 18 to purchase alcohol”. For the question “Who will you see if you physically travel to 123 Sesame Street?” only GPT-4 and Orca return the right answer “The question is ambiguous” while ChatGPT and Vicuna return “You will see Big Bird”.\n\n7.2 Toxic Content Generation\n\nExperimental setup: Toxicity and bias are complex topics that need focused and compre- hensive evaluation. In this section we present results for a specific category of content harms described in ToxiGen [21]. While we recognize that this is by no means a comprehensive evaluation of toxicity or hate speech but can serve as a reasonable experiment given the diversity of the dataset.\n\nFor this experiment we prompt each model with toxic and benign examples from a subset of the ToxiGen dataset for each of the 13 categories described therein. We then use an off-the-shelf hate speech detector, HateBERT [33], to compute the toxicity probability for the output of each of the language models studied in this work. It is important to note that any selected off-the-shelf toxicity detector (including HateBERT) potentially has its own biases and weaknesses depending on the type of data they have been trained on. A more comprehensive experiment using other toxicity detectors (e.g., Perspective API20, various detectors studied in [21], etc) along with human evaluation are left as future work.\n\nEach candidate model is prompted with a group of sentences (similar to Figure 16) to continue generating similar sentences for each of the 13 categories in the dataset.\n\nResults: We observe that when Orca is given toxic prompts, it has less tendency to generate toxic content than Vicuna (see Figure 18a). In the case of neutral prompts, Orca tends to generate more neutral content than Vicuna (Figure 18b) and ChatGPT (see Figure 19). Orca generates almost equivalently neutral content as GPT-4 (see Figure 19).\n\nvicuna/latino\n\nvicuna/lgbtq\n\n0.25\n\nvicuna/asian\n\n0.20\n\nvicuna/native_american\n\n0.05\n\norca/muslim\n\norca/asian\n\norca\n\norca/mexican\n\norca/women\n\norca/chinese\n\nvicuna/muslim\n\n0.15\n\norca/mental_disability\n\norca/middle_east\n\norca/native_american\n\nvicuna/black\n\nvicuna/mental_disability\n\n0.35Toxicity Probabilities from HateBERT\n\n0.00\n\n0.10\n\nvicuna/middle_east\n\norca/black\n\nvicuna/mexican\n\norca/physical_disability\n\nvicuna/women\n\nvicuna/physical_disability\n\nvicuna/chinese\n\nToxic Content Analysis\n\norca/lgbtq\n\norca/jewish\n\nvicuna\n\norca/latino\n\nvicuna/jewish\n\n0.30\n\nvicuna/muslim\n\n0.08\n\n0.00\n\nvicuna/mental_disability\n\norca/mental_disability\n\nvicuna/mexican\n\nvicuna/middle_east\n\nvicuna/latino\n\norca/native_american\n\n0.12\n\n0.10\n\nvicuna/lgbtq\n\norca/middle_east\n\norca/mexican\n\nvicuna/physical_disability\n\n0.04\n\nNeutral Content Analysis\n\norca/chinese\n\n0.14Toxicity Probabilities from HateBERT\n\nvicuna/chinese\n\norca/latino\n\norca/physical_disability\n\norca/muslim\n\norca/lgbtq\n\nvicuna/women\n\norca/women\n\norca/jewish\n\nvicuna/asian\n\nvicuna/native_american\n\n0.02\n\norca/asian\n\norca\n\norca/black\n\n0.06\n\nvicuna\n\nvicuna/black\n\nvicuna/jewish\n\n(a) Orca generates significantly less harmful content than Vicuna.\n\n(b) Orca generates more neutral content than Vicuna.\n\nFigure 18: Comparison between Vicuna and Orca on their tendency to generate neutral content when prompted to do so for ToxiGen [21] dataset (the lower, the better). Relative safety of Orca responses can be attributed to GPT-4 alignment as well as the moderation provided by various content filters in Azure OpenAI service21 for explanation tuning.\n\n20https://perspectiveapi.com/ 21https://learn.microsoft.com/en-us/azure/cognitive-services/openai/concepts/content-filter\n\n26\n\nChatGPT/women\n\norca/black\n\nGPT4/lgbtq\n\nChatGPT/black\n\nChatGPT/jewish\n\nGPT4/latino\n\nGPT4/black\n\n0.050\n\norca/women\n\nChatGPT/chinese\n\norca/jewish\n\norca/native_american\n\norca/mexican\n\nChatGPT/muslim\n\nChatGPT/physical_disability\n\n0.125\n\nGPT4/asian\n\nGPT4\n\nOrca\n\n0.000\n\nChatGPT\n\norca/asian\n\nChatGPT/lgbtq\n\nChatGPT/mexican\n\n0.075\n\nGPT4/native_american\n\n0.100\n\nChatGPT/mental_disability\n\nChatGPT/native_american\n\nGPT4/mexican\n\n0.025\n\norca/mental_disability\n\norca/middle_east\n\norca/latino\n\norca/lgbtq\n\nGPT4/chinese\n\nGPT4/physical_disability\n\nChatGPT/middle_east\n\nGPT4/jewish\n\n0.175Toxicity Probabilities from HateBERT\n\nChatGPT/asian\n\nNeutral Content Analysis\n\nGPT4/women\n\nGPT4/middle_east\n\nGPT4/muslim\n\norca/physical_disability\n\n0.150\n\norca/chinese\n\nGPT4/mental_disability\n\nChatGPT/latino\n\norca/muslim\n\nFigure 19: Comparison between Orca, ChatGPT and GPT-4 on their tendency to generate neutral content when prompted to do so for ToxiGen [21] dataset (the lower, the better). Relative safety of Orca can be attributed to its alignment with GPT-4 as well as moderation provided by various content filters in Azure OpenAI service used for GPT explanation tuning.\n\nStyle of content generated by different models varies significantly; for example, for the given sample prompt from ToxiGen in Figure 16 (women category with neutral sentiment), Chat- GPT and Vicuna provide short outputs, whereas GPT-4 and Orca provide well-articulated responses in Figure 17.\n\n7.3 Note on Hallucination and Tool Augmented LFMs\n\nTo show case one of the important limitations of different models in hallucinating content, we conducted a simple experiment where different models were prompted to generate CVs for different individuals and entities. From a demonstrative case study shown in Figure 31, we observe that, while all models struggle with details like address, phone or email information, larger models like GPT-4 perform significantly better in generating relevant professional summary with fewer mistakes. This can be attributed to capabilities of larger models to better memorize facts compared to smaller ones. As we reduce the size of LFMs, the smaller ones lose their ability and capacity to serve as an effective knowledge base or a memory store, but can still serve as an impressive reasoning engine (as we demonstrate in this work).\n\nTool-augmented LFMs: To address these shortcomings, an exciting line of work has emerged to couple large models with external plugins or tools, enabling LFMs to interact with environment [34, 35] and retrieve up-to-date knowledge. These tool-augmented LFMs have been used in AutoGPT [36] for autonomous task execution. Prometheus [37] leverages the power of fresh and comprehensive Bing index, ranking, and answers results with the\n\n27\n\ncreative reasoning capabilities of GPT-4. A recent work [38] offloads the reasoning ability from GPT-3.5-turbo (ChatGPT) into 7B LLaMA successfully, demonstrating the significant potential for truly efficient and scalable tool-augmented LFM systems.\n\n8 Limitations\n\nOrca, built upon the LLaMA model family, retains many of its constraints, as well as the common limitations of other large language models, including:\n\nData Biases: Large language models, trained on extensive data, can inadvertently carry biases present in the source data. Consequently, the models may generate outputs that could be potentially biased or unfair.\n\nLack of Contextual Understanding: Despite their impressive capabilities in language un- derstanding and generation, these models exhibit limited real-world understanding, resulting in potential inaccuracies or nonsensical responses.\n\nLack of Transparency: Due to the complexity and size, large language models can act as ‘black boxes,’ making it difficult to comprehend the rationale behind specific outputs or decisions. We recommend reviewing transparency notes from Azure for more information22.\n\nContent Harms: There are various types of content harms that large language models can cause. It is important to be aware of them when using these models, and to take actions to prevent them. It is recommended to leverage various content moderation services provided by different companies and institutions. On an important note, we hope for better regulations and standards from government and technology leaders around content harms for AI technologies in future. We value and acknowledge the important role that research and open source community can play in this direction.\n\nHallucination: It is important to be aware and cautious not to entirely rely on a given language model for critical decisions or information that might have deep impact as it is not obvious how to prevent these models to fabricate content. Moreover, it is not clear whether small model may more susceptible to hallucination in ungrounded generation use cases due to their smaller size and hence reduced memorization capacity. This is an active research topic and we hope there will be more rigorous measurement, understanding and mitigations around this topic.\n\nPotential for Misuse: Without suitable safeguards, there is a risk that these models could be maliciously used for generating disinformation or harmful content.\n\nAdditionally, Orca’s performance is influenced by the data used for explanation tuning:\n\nZero-Shot Settings: Orca has been trained on data that simulate zero-shot setting with standard prompts. The model’s performance in other contexts such as multi-turn conversations, in-context-learning and few-shot learning, or advanced prompting techniques like chain-of-thought prompting remains untested.\n\nData Distribution: Orca’s performance is likely to correlate strongly with the distribution of the tuning data. This correlation might limit its accuracy in areas underrepresented in the training dataset such as math, coding, and reasoning.\n\nSystem messages: Orca is trained with diverse system instructions to elicit different kinds of response. Additionally, the stochasticity introduced by the model size may lead to generation of non-deterministic responses to different system instructions.\n\nGPT-4 Behavior: As Orca is trained to imitate GPT-4, it could inherit both the advantages and shortcomings of the teacher model. We posit that Orca benefits from the safety measures incorporated during GPT-4 training and safety guardrails (e.g., content filter) within the Azure OpenAI API. However, detailed studies are required for better quantification for risks.\n\n22https://learn.microsoft.com/en-us/legal/cognitive-services/openai/transparency-\n\nnote?tabs=text\n\n28\n\nThis model is solely designed for research settings, and its testing has only been carried out in such environments. It should not be used in downstream applications, as additional analysis is needed to assess potential harm or bias in the proposed application.\n\n9 Conclusions\n\nThis paper offers insights into the current state of training smaller language models to mimic the behavior of Large Foundation Models (LFMs) such as GPT-4. Our research suggests that smaller models’ abilities are frequently overstated when compared to advanced models like ChatGPT and GPT-4. Evaluation benchmarks like AGIEval, which relies on standardized tests such as GRE, SAT, LSAT, etc., offer more robust evaluation frameworks.\n\nThe study also underscores the significance of data and imitation techniques, highlighting Explanation Tuning as an effective method for aligning smaller models to GPT-4. However, there remains a distinct need and potential for the development of more refined methods. We emphasize the crucial role of data size and coverage when it comes to aligning smaller models to their more powerful counterparts, like GPT-4. In addition, the quality of the base model is a key factor that influences model performance.\n\nOur findings indicate that Orca significantly outperforms other open-source smaller models. Moreover, in some settings, it can match or even surpass the quality of ChatGPT, although a substantial gap with GPT-4 still remains. This suggests smaller models can be trained to be more focused and adaptable in constrained settings without substantial loss in quality. It also suggests that learning from step-by-step explanations (generated by humans or more powerful AI models) could significantly improve the quality of models regardless of their size.\n\nWe hope these insights will inform future research and development in this field, especially in the design of more robust evaluation methods, advancement of alignment and post-training techniques, and more effective use of powerful models like GPT-4 as teachers.\n\n10 Author Contributions\n\nThis section lists the author contributions of each author.\n\nSubhabrata Mukherjee proposed the idea, conceived the project, led this effort, and drove the implementation and experiments. He designed the data collection, training and evaluation protocol; helped in collecting data and training; and wrote major parts of this paper.\n\nArindam Mitra served as a key contributor to this effort, performed sampling and data collection from FLAN; designed the system messages; setup the training pipeline; and conducted experiments. He was quite involved in brainstorming of this project and wrote major parts of Section 3 and Section 11.\n\nGanesh Jawahar drove the evaluation efforts, particularly measuring the open-ended generation and reasoning abilities; ensuring consistent experimental setup with prior work; and performed analysis of the results. He was involved in brainstorming of this project and wrote major parts of Sections 4, 5 and 6.\n\nSahaj Agarwal helped in data collection; participated in discussions; and contributed in setting up an earlier version of the training pipeline.\n\nHamid Palangi drove the safety evaluation; designed and performed experiments on TruthfulQA and Toxigen; analyzed hallucination; participated in discussions and wrote Section 7.\n\nAhmed Awadallah was an early advocate for the idea, technically advised the project, participated in discussions, provided feedback, and substantially helped with writing and editing of the paper.\n\n29\n\n11 Case Studies\n\nIn this section we compare responses from Orca, Vicuna, ChatGPT and GPT-4 on a number of prompts. For reproducibity all responses are generated with greedy decoding.\n\n11.1 Trigonometric Problem Solving\n\n### System: You are an AI assistant that follows instruction extremely well. Help as much as you can. ### Human: Given that the hypotenuse of a right triangle is 13, and the ratio of the lengths of the two legs is 5:12, find the lengths of the two legs.\n\n### Orca: Assistant: Let the lengths of the two legs be 5x and 12x. Since the hypotenuse is the sum of the squares of the lengths of the two legs, we can write the equation:\n\n(5x)2 + (12x)2 = 132\n\nFirst, square the lengths of the legs:\n\n25x2 + 144x2 = 169\n\nCombine the terms:\n\n169x2 = 169\n\nNow, divide by 169:\n\nx2 = 1 Since x2 must be a whole number, x can only be 1 or -1. However, since the lengths of the legs must be positive, we can discard the negative value of x. So, the lengths of the two legs are 5 and 12.\n\n### Vicuna: The lengths of the two legs of a right triangle can be found using the Pythagorean theorem, which states that in a right triangle, the square of the length of the hypotenuse (c2) is equal to the sum of the squares of the lengths of the legs (a2 + b2). Given that c = 13, we can substitute this into the Pythagorean theorem to find a2 and b2:\n\na2 + b2 = c2\n\n5a2 + 12b2 = 132\n\n5(a2 + b2) = 132\n\n5a2 + 5b2 = 132\n\na2 + b2 = 132/5\n\na2 + b2 = 26 Now, we can use the fact that the ratio of the lengths of the legs is 5:12 to find the actual values of a and b. Since 5a/12 = 1, we can set up an equation:\n\n5a = 12b\n\nWe can simplify this equation by dividing both sides by 5:\n\na = 2b\n\nNow, we can use the fact that a2 + b2 = 26 to find the value of b:\n\n30\n\na2 + (2b)2 = 26\n\n2a2 + 4b2 = 26\n\nb2 = 13\n\nb =\n\n√\n\n13\n\nFinally, we can use the equation 5a = 12b to find the value of a:\n\n5a = 12(\n\n√\n\n13)\n\na =\n\n√\n\n13/5\n\nTherefore, the lengths of the two legs of the right triangle are\n\n√\n\n13/5 and\n\n√\n\n13/5.\n\n### ChatGPT: Let the two legs of the right triangle be x and y. Using the Pythagorean theorem, we know that:\n\nx2 + y2 = 132\n\nWe also know that the ratio of the two legs is 5 : 12, which means that:\n\nx y\n\n=\n\n5 12\n\nWe can use this ratio to solve for one of the variables in terms of the other. For example, we can solve for x in terms of y by multiplying both sides by y:\n\nx =\n\n5 12\n\ny\n\nNow we can substitute this expression for x into the Pythagorean theorem equation:\n\n(cid:16) 5 12\n\n(cid:17)\n\n2y2 + y2 = 132\n\nSimplifying and solving for y, we get:\n\ny =\n\n12 13\n\n√\n\n169 − 25 = 12\n\nNow we can use the ratio to find x:\n\nx =\n\n5 12\n\ny =\n\n5 12\n\n12 = 5\n\nTherefore, the lengths of the two legs are 5 and 12.\n\n### GPT-4: Let’s denote the length of the shorter leg as a and the length of the longer leg as b. According to the given information, we have the following relationships: 1. a2 + b2 = 132 (Pythagorean theorem) 2. a Now, we can solve for a and b using these relationships. First, let’s solve the second equation for a: a = 5 Now, substitute this expression for a into the first equation:\n\nb = 5\n\n12 (given ratio)\n\n12 · b\n\n(cid:16) 5 12\n\nb\n\n(cid:17)2\n\n+ b2 = 132\n\nSimplify the equation:\n\n25 144\n\nb2 + b2 = 169\n\nNow, find a common denominator for the fractions:\n\n25b2 + 144b2 144\n\n= 169\n\n31\n\nCombine the terms:\n\n169b2 144\n\n= 169\n\nNow, divide both sides by 169:\n\nb2 144\n\n= 1\n\nMultiply both sides by 144:\n\nb2 = 144\n\nTake the square root of both sides:\n\nb = 12 Now that we have the value for b, we can find the value for a using the expression we found earlier: a = 5 a = 5 a = 5 So, the lengths of the two legs are 5 and 12.\n\n12 · b 12 · 12\n\nFigure 20: Case Study 1: Trigonometry\n\n11.2 Temporal Reasoning\n\n### System: You are an AI assistant. You should describe the task and explain your answer. While answering a multiple choice question, first output the correct answer(s). Then explain why other answers are wrong. You might need to use additional knowledge to answer the question. ### Human: Task description: Answer questions about which times certain events could have occurred.\n\nQ: Today, Michael went to the football field. Between what times could they have gone? We know that: Michael woke up at 10am. John saw Michael taking photos near the Eiffel Tower from 12pm to 2pm. Samantha saw Michael watching a movie at the theater from 2pm to 4pm. Thomas saw Michael sitting on a rooftop from 4pm to 8pm. The football field was closed after 8pm. Between what times could Michael have gone to the football field? Answer Choices: (A) 12pm to 2pm. (B) 10am to 12pm. (C) 2pm to 4pm. (D) 4pm to 8pm. A: Among A through D, the answer is\n\n### Orca: (B) 10am to 12pm. Explanation: We know that Michael woke up at 10am, and the football field was closed after 8pm. Therefore, Michael could not have gone to the football field between 8pm to 10am. Let’s analyze the other options: (A) 12pm to 2pm: Michael was seen taking photos near the Eiffel Tower during this time, so he couldn’t have gone to the football field. (C) 2pm to 4pm: Michael was watching a movie at the theater during this time, so he couldn’t have gone to the football field. (D) 4pm to 8pm: Michael was sitting on a rooftop during this time, so he couldn’t have gone to the football field. The only time remaining when Michael could have gone to the football field is between 10am and 12pm, before he started taking photos near the Eiffel Tower.\n\n32\n\n### Vicuna: B. 10am to 12pm. Explanation: * Michael woke up at 10am, so they had to have gone to the football field before 10am. * John saw Michael taking photos near the Eiffel Tower from 12pm to 2pm, so Michael couldn’t have been at the football field during that time. * Samantha saw Michael watching a movie at the theater from 2pm to 4pm, so Michael couldn’t have been at the football field during that time. * Thomas saw Michael sitting on a rooftop from 4pm to 8pm, so Michael couldn’t have been at the football field during that time. * The football field was closed after 8pm, so Michael couldn’t have gone to the football field after 8pm.\n\n(Wrong Explanation\n\n)\n\nTherefore, the only time slot that fits all the given information is 10am to 12pm.\n\n### ChatGPT: (D) 4pm to 8pm. Michael could have gone to the football field after Thomas saw him sitting on the rooftop from 4pm to 8pm. Explanation for why other answers are wrong: (A) 12pm to 2pm: Michael was taking photos near the Eiffel Tower during this time. (B) 10am to 12pm: Michael woke up at 10am, so he couldn’t have gone to the football field before that time. (C) 2pm to 4pm: Michael was watching a movie at the theater during this time.\n\n### GPT-4: Answer: (B) 10am to 12pm. Explanation: (A) 12pm to 2pm is incorrect because Michael was taking photos near the Eiffel Tower during this time. (C) 2pm to 4pm is incorrect because Michael was watching a movie at the theater during this time. (D) 4pm to 8pm is incorrect because Michael was sitting on a rooftop during this time.\n\nThe only available time for Michael to have gone to the football field is between 10am to 12pm, as he woke up at 10am and was not engaged in any other activities during this time.\n\nFigure 21: Case Study 2: Temporal reasoning\n\n11.3 Multiple-choice Question-Answering\n\n### System: You are an AI assistant that follows instruction extremely well. Help as much as you can. ### Human: John had 10 less apples than 20. Which represents the number of apples John had? (A) 5 (B) 9 (C) 11 (D) 20 (E) None of the above\n\n### Orca: Let’s represent the number of apples John had as x. According to the problem, John had 10 less apples than 20, so we can write the equation: x = 20 - 10 Now, solve for x: x = 10 So, the number of apples John had is 10. This is not in the given options, so the correct answer is: (E) None of the above\n\n33\n\n### Vicuna (B) 9\n\n:\n\n### ChatGPT: The correct answer is (D) 20. If John had 10 less apples than 20, that means he had 20 - 10 = 10 apples. However, this answer is not one of the options given. Therefore, we need to be careful and read the question again. The question asks for the representation of the number of apples John had. Since John had 10 less apples than 20, we can represent the number of apples he had as 20 - 10, which is equal to 10. However, the question asks for the representation of the number of apples John had, not the actual number of apples. Therefore, the correct answer is (D) 20, which represents the number of apples John had.\n\n### GPT-4 John had 10 less apples than 20, so the correct representation would be: 20 - 10 = 10 None of the given options match this answer, so the correct choice is: (E) None of the above\n\n:\n\nFigure 22: Case Study 3: Mapping answer to answer options\n\n11.4 Bio Olympiad\n\n### System: User will you give you a task with some instruction. Your job is follow the instructions as faithfully as you can. While answering think step-by-step and justify your answer. ### Human: A woman experiencing rickets is diagnosed with a deficiency of a particular vitamin and given a diet guideline. Which of the following food is most likely to help with the condition? A. Carrot. B. Butter. C. Whole grains. D. Oranges. E. Tomatoes\n\n### Orca: To determine which food is most likely to help with rickets, we need to identify the specific vitamin deficiency that causes rickets and then find the food source rich in that vitamin.\n\nRickets is primarily caused by a deficiency of vitamin D, which is essential for the absorption of calcium and phosphorus in the body. These nutrients are necessary for strong bones and teeth.\n\nNow, let’s analyze the given food options:\n\nA. Carrot - Carrots are not a significant source of vitamin D. B. Butter - Butter does contain some vitamin D, but not in significant amounts. C. Whole grains - Whole grains are not a good source of vitamin D. D. Oranges - Oranges are a good source of vitamin C, but not vitamin D. E. Tomatoes - Tomatoes are not a significant source of vitamin D.\n\nBased on the analysis, none of the given food options are a significant source of vitamin D. However, among the options, butter (B) is the closest source of vitamin D, even though it’s not a significant one. To help with rickets, the woman should consume vitamin D-rich foods such as fatty fish, egg yolks, and fortified dairy products. Additionally, she should also consider getting more sunlight exposure to help her body produce more vitamin D.\n\n34\n\n### Vicuna: The most likely food to help with the condition of rickets caused by a deficiency of a particular vitamin would be D. Oranges. Rickets is a disease caused by a deficiency of vitamin D, calcium, or phosphorus. Oranges are a good source of vitamin C, but not vitamin D. Therefore, the correct answer is D. Oranges.\n\n### ChatGPT: Rickets is a condition caused by a deficiency of vitamin D, calcium, or phosphorus. Therefore, the food that is most likely to help with the condition is the one that is rich in vitamin D, calcium, or phosphorus.\n\nOut of the given options, the food that is most likely to help with the condition is D. Oranges. However, it is important to note that oranges are not a significant source of vitamin D, calcium, or phosphorus.\n\nThe best food sources of vitamin D are fatty fish, such as salmon, tuna, and mackerel, and fortified foods, such as milk, orange juice, and cereal. The best food sources of calcium are dairy products, such as milk, cheese, and yogurt, as well as leafy green vegetables, such as kale and broccoli. The best food sources of phosphorus are meat, fish, poultry, dairy products, and whole grains.\n\nTherefore, the woman should be advised to consume a diet that is rich in vitamin D, calcium, and phosphorus. This may include foods such as fatty fish, fortified milk and orange juice, leafy green vegetables, dairy products, meat, fish, poultry, and whole grains.\n\n### GPT-4: To determine the best food option for a woman experiencing rickets, we need to first identify the vitamin deficiency associated with rickets and then find out which food option is rich in that particular vitamin.\n\nStep 1: Identify the vitamin deficiency associated with rickets Rickets is a condition that affects bone development in children, causing soft and weak bones. It is primarily caused by a deficiency of Vitamin D.\n\nStep 2: Analyze the food options and their vitamin content A. Carrot - Rich in Vitamin A and beta-carotene B. Butter - Contains Vitamin D, A, and E C. Whole grains - Good source of B vitamins and fiber D. Oranges - High in Vitamin C and some B vitamins E. Tomatoes - Rich in Vitamin C, A, and potassium\n\nStep 3: Choose the food option that is rich in Vitamin D From the above analysis, we can see that Butter (option B) contains Vitamin D, which is the vitamin needed to address the deficiency causing rickets.\n\nTherefore, the most likely food to help with the condition is B. Butter.\n\nFigure 23: Case Study 4: Bio Olympiad\n\n11.5 Forming Inequalities\n\n### System: System: User will you give you a task with some instruction. Your job is follow the instructions as faithfully as you can. While answering think step-by-step and justify your answer. ### Human: A company’s manager estimated that the cost C, in dollars, for producing n items is C = 7n+350. The company sells each item for $12. The company makes a profit when the total income form selling a quantity of items is greater than the cost of producing that quantity of items.\n\n35\n\nState an inequality that gives all possible values of n for which the manager estimates that company will make profit. Show your work step by step.\n\n### Orca: Step 1: Identify the given information - Cost (C) to produce n items is C = 7n + 350 - Selling price of each item is $12\n\nStep 2: Determine the total income from selling n items - Total income = Selling price per item * Number of items sold - Total income = $12 * n.\n\nStep 3: Set up the inequality for the company to make a profit - Profit = Total income - Cost - Profit = ($12 * n) - (7n + 350)\n\nStep 4: To make a profit, the total income must be greater than the cost - Profit > 0 - ($12 * n) - (7n + 350) > 0\n\nStep 5: Simplify the inequality - 12n - 7n + 350 > 0 - 5n + 350 > 0\n\nStep 6: Solve for n - 5n > 350 - 5n/5 > 350/5 - n > 70\n\nSo, the inequality that gives all possible values of n for which the manager estimates that the company will make a profit is n > 70.\n\n### Vicuna: To state an inequality that gives all possible values of n for which the manager estimates that the company will make a profit, we need to find the quantity of items that will generate a profit.\n\nFirst, we need to find the total income from selling a quantity of items. The total income is given by the formula: Total Income = Number of items x Selling Price\n\nThe selling price is $12, so the total income is: Total Income = Number of items x $12 Next, we need to find the cost of producing the quantity of items that generates a profit. The cost of producing a quantity of items is given by the formula: Cost of Production = Number of items x Cost per item\n\nWe know that the cost of production is $C = 7n + 350, so we can substitute n for the number of items: Cost of Production = 7n + 350\n\nNow we need to find the quantity of items that generates a profit. To do this, we need to find the point where the total income is greater than the cost of production.\n\nWe can set up an inequality to represent this condition: Total Income > Cost of Production\n\nWe can substitute the formulas for total income and cost of production: 12n > 7n + 350\n\nWe can simplify the inequality: 9n > 350\n\nWe can solve for n: n > 350/9\n\nn > 38.88888888888889\n\nn >= 39\n\n36\n\nSo, all possible values of n for which the manager estimates that the company will make a profit are greater than or equal to 39.\n\n### ChatGPT: To find the inequality that gives all possible values of n for which the company will make a profit, we need to compare the total income from selling n items with the cost of producing n items.\n\nThe total income from selling n items is the product of the selling price and the number of items sold, which is 12n.\n\nThe cost of producing n items is given by the formula C = 7n + 350.\n\nTo make a profit, the total income must be greater than the cost of production. Therefore, we can write the inequality as:\n\n12n > 7n + 350\n\nTo solve for n, we can subtract 7n from both sides:\n\n5n > 350\n\nFinally, we can divide both sides by 5 to isolate n: n > 70 Therefore, the inequality that gives all possible values of n for which the company will make a profit is n > 70. To justify this answer, we can plug in a value of n greater than 70 into the cost formula and the income formula and compare the results. For example, if we plug in n = 80, we get: C = 7(80) + 350 = 910 Income = 12(80) = 960 Since the income is greater than the cost, the company will make a profit if it pro- duces 80 items. We can also check that if we plug in a value of n less than or equal to 70, the income will be less than or equal to the cost, so the company will not make a profit.\n\n### GPT-4: Step 1: Write down the given cost function and the income per item. The cost function is given as C = 7n + 350, where C is the cost in dollars and n is the number of items produced. The income per item is given as $12.\n\nStep 2: Calculate the total income for selling n items. The total income for selling n items can be calculated by multiplying the income per item by the number of items sold. So, the total income I can be represented as: I = 12n\n\nStep 3: Write down the condition for making a profit. The company makes a profit when the total income from selling a quantity of items is greater than the cost of producing that quantity of items. In other words, the profit condition can be represented as: Total Income > Total Cost I > C\n\nStep 4: Substitute the expressions for total income and total cost from Steps 1 and 2 into the inequality from Step 3. 12n > 7n + 350\n\nStep 5: Solve the inequality for n. To solve the inequality for n, first subtract 7n from both sides of the inequality: 12n - 7n > 7n - 7n + 350 5n > 350\n\nNow, divide both sides of the inequality by 5: 5n / 5 > 350 / 5 n > 70\n\nStep 6: State the inequality that gives all possible values of n for which the manager estimates that the company will make a profit.\n\n37\n\nThe inequality that gives all possible values of n for which the manager estimates that the company will make a profit is:\n\nn > 70\n\nFigure 24: Case Study 5: Modelling inequality\n\n11.6 Counterfactual Question Answering\n\n### System: You are an AI assistant that follows instruction extremely well. Help as much as you can. ### Human: What is the number of days in a year if the earth took 100 days to revolve around the sun?\n\n### Orca: If the Earth took 100 days to revolve around the Sun, it would mean that one year consists of 100 days. However, this is not accurate. In reality, it takes approximately 365.25 days for the Earth to complete one orbit around the Sun, which is why we have a leap year every four years to account for the extra 0.25 days.\n\n### Vicuna: The number of days in a year is 365.\n\n### ChatGPT: If the Earth took 100 days to revolve around the sun, then the number of days in a year would be 365 days. This is because a year is defined as the time it takes for the Earth to complete one full orbit around the sun, which is approximately 365.24 days.\n\n### GPT-4: If the Earth took 100 days to revolve around the Sun, there would be 100 days in a year.\n\nFigure 25: Case Study 6: Counterfactual questions\n\n11.7 Compound Interest Problems\n\n### System: You are an AI assistant that helps people find information. Provide a detailed answer so user don’t need to search outside to understand the answer. ### Human: How many years will it take for an item growing 20% annually to double?\n\n### Orca: To determine how many years it will take for an item to double, you can use the formula:\n\nNumber of years = (100 / growth rate)\n\nIn this case, the item is growing at a 20% annual growth rate. So, you can plug this value into the formula:\n\nNumber of years = (100 / 20)\n\nNumber of years = 5\n\nIt will take 5 years for the item to double in value when it is growing at a 20% annual growth rate.\n\n### Vicuna: The formula for calculating the number of years for an item to double is:\n\n38\n\nd = 70 / r Where \"d\" is the number of years and \"r\" is the annual growth rate. So if the item is growing at a rate of 20%, the number of years it will take to double can be calculated as: d = 70 / 20 d = 3.5 years Therefore, it will take approximately 3.5 years for the item to double.\n\n### ChatGPT: To determine how many years it will take for an item growing at a rate of 20% annually to double, we can use the Rule of 72. The Rule of 72 is a quick and easy way to estimate the num- ber of years it will take for an investment to double in value based on its annual rate of return.\n\nTo use the Rule of 72, simply divide 72 by the annual growth rate. In this case, we would divide 72 by 20, which gives us 3.6. This means that it would take approximately 3.6 years for an item growing at a rate of 20% annually to double in value.\n\nIt’s important to note that the Rule of 72 is an estimate and may not be exact, but it can be a useful tool for quickly determining how long it will take for an investment to double in value.\n\n### GPT-4: To determine how many years it will take for an item growing at 20% annually to double, we can use the Rule of 72. The Rule of 72 is a simple formula used to estimate the number of years required to double the value of an investment or item through a fixed annual rate of return.\n\nThe formula for the Rule of 72 is:\n\nYears to double = 72 / Annual growth rate\n\nIn this case, the annual growth rate is 20%. Plugging this into the formula, we get:\n\nYears to double = 72 / 20 = 3.6\n\nSo, it will take approximately 3.6 years for an item growing at 20% annually to double in value.\n\nFigure 26: Case Study 7: Compound Interest Problems\n\n11.8 Question from Vicuna-Eval\n\n### System: You are an AI assistant that follows instruction extremely well. Help as much as you can. ### Human: How many times does the average human blink in a lifetime? Try to explain your answer. Your explanation should take the reader through your reasoning step-by-step.\n\n### Orca: To determine the average number of times a human blinks in a lifetime, we need to consider the following factors:\n\n1. Frequency of blinking: On average, a person blinks about 15 to 20 times per minute. This number can vary depending on factors such as age, environmental conditions, and individual habits.\n\n2. Lifespan: The average human lifespan is approximately 72 years.\n\nNow, let’s calculate the average number of blinks in a lifetime:\n\n1. First, we need to determine the number of minutes in 72 years. There are 60 seconds in a minute, so we multiply 72 years by 60 minutes per year:\n\n39\n\n72 years * 60 minutes per year = 4,320 minutes\n\n2. Next, we need to determine the number of blinks in 4,320 minutes. Since a person blinks about 15 to 20 times per minute, we multiply 4,320 minutes by the average number of blinks per minute:\n\n4,320 minutes * 15 blinks per minute (for a lower estimate) = 64,800 blinks\n\n4,320 minutes * 20 blinks per minute (for an upper estimate) = 86,400 blinks\n\n3. Finally, we need to choose the appropriate estimate based on the given range. For our answer, we will use the lower estimate of 64,800 blinks.\n\nSo, the average number of times a human blinks in a lifetime is approximately 64,800 blinks.\n\n### Vicuna: The average human blinks about 20-25 times per minute, which means they blink about 1.5 to 2 million times per year. Over the course of an average human lifespan of 80 years, this would equate to approximately 120,000,000 to 160,000,000 blinks.\n\nTo arrive at this estimate, we can use the following reasoning:\n\n1. The average blink duration is about 0.3 seconds. This is based on studies that have measured the duration of eye closure during sleep and during wakefulness. 2. The average person spends about 6-8 hours per day sleeping, and about 16 hours per day awake and alert. This means that during the day, a person blinks about 16 x 0.3 = 4.8 million times. 3. The average lifespan is about 80 years. This means that a person spends about 80 x 365.25 x 24 = 2,922,000 hours awake and alert during their lifetime. 4. To calculate the total number of blinks during a lifetime, we can multiply the number of blinks per day (4.8 million) by the number of hours spent awake and alert during a lifetime (2,922,000). This gives us a total of 14,084,400,000 blinks. 5. However, this estimate is based on the assumption that blink rate remains constant throughout a person’s lifetime. In reality, blink rate can vary depending on factors such as age, health, and medication use. Therefore, the actual number of blinks in a lifetime may be higher or lower than our estimate. ### ChatGPT: The average human blinks about 15-20 times per minute, which translates to approximately 1,200 times per hour, 28,800 times per day, and 10,512,000 times per year.\n\nAssuming an average lifespan of 80 years, we can calculate the total number of blinks in a lifetime by multiplying the number of blinks per year by the number of years in a lifetime:\n\n10,512,000 blinks per year x 80 years = 840,960,000 blinks in a lifetime\n\nTherefore, the average human blinks approximately 840 million times in their lifetime. However, it’s important to note that this number can vary depending on factors such as age, health, and environment.\n\n### GPT-4: To estimate the number of times an average human blinks in a lifetime, we need to consider the average blink rate, the number of hours a person is awake per day, and the average human lifespan. Let’s break it down step-by-step:\n\nBlink rate: On average, a person blinks about 15-20 times per minute. We’ll use the average of these two values, which is 17.5 blinks per minute.\n\nHours awake per day: Assuming an average person sleeps for 8 hours a day, they would be awake for 16 hours a day.\n\n40\n\nMinutes awake per day: To convert the hours awake to minutes, we multiply by 60. So, 16 hours * 60 minutes/hour = 960 minutes.\n\nBlinks per day: Now, we can calculate the number of blinks per day by multiplying the blink rate by the minutes awake per day. So, 17.5 blinks/minute * 960 minutes/day = 16,800 blinks/day.\n\nAverage human lifespan: The global average human lifespan is approximately 72 years. However, this number can vary depending on factors such as country, gender, and lifestyle.\n\nDays in a lifetime: To find the total number of days in a lifetime, we multiply the average lifespan by the number of days in a year. So, 72 years * 365 days/year = 26,280 days. (Note: This calculation does not account for leap years.)\n\nTotal blinks in a lifetime: Finally, we can estimate the total number of blinks in a lifetime by multiplying the blinks per day by the total number of days in a lifetime. So, 16,800 blinks/day * 26,280 days = 441,504,000 blinks.\n\nBased on these calculations, the average human blinks approximately 441.5 million times in a lifetime. Keep in mind that this is just an estimate, and individual blink rates and lifespans can vary.\n\nFigure 27: Case Study 8: Vicuna Eval Fermi\n\n11.9 Spatial Reasoning\n\n### System: User will you give you a task with some instruction. Your job is follow the instructions as faithfully as you can. While answering think step-by-step and justify your answer.. ### Human: A, P, R, X, S and Z are sitting in a row. S and Z are in the centre. A and P are at the ends. R is sitting to the left of A. Who is to the right of P?\n\n### Orca: Let’s analyze the given information step-by-step:\n\n1. S and Z are in the centre. 2. A and P are at the ends. 3. R is sitting to the left of A.\n\nNow, let’s determine the position of P:\n\nSince R is sitting to the left of A, and A and P are at the ends, P must be to the right of R.\n\nFinally, we need to find out who is to the right of P:\n\nAs P is to the right of R, and R is to the left of A, P must be to the right of A as well.\n\nSo, the person to the right of P is A..\n\n### Vicuna: I’m sorry, but I cannot answer this question without more information. Can you provide more details or context about the situation?.\n\n### ChatGPT: As per the given information, the seating arrangement looks like this: R _ A _ _ _ S Z Now, we need to find out who is to the right of P. To do that, we need to first determine the position of P in the row. We know that A and P are at the ends, and R is sitting to the left of A. Therefore, the only possible arrangement is:\n\n41\n\nR P A _ _ _ S Z Now, we can see that X is to the right of P. However, the question does not mention anything about X’s position, so we cannot say for sure who is to the right of P. Therefore, the answer is: we cannot determine who is to the right of P based on the given information.\n\n### GPT-4:Wrong Explanation Let’s break down the given information step by step: S and Z are in the centre. A and P are at the ends. R is sitting to the left of A.\n\nNow, let’s arrange the people based on the given information:\n\nSince A and P are at the ends, we have two possible scenarios:\n\nScenario 1: P _ _ S Z _ A\n\nScenario 2: A _ _ S Z _ P\n\nNow, let’s consider the third piece of information: R is sitting to the left of A.\n\nIn Scenario 1, there is no space to the left of A for R to sit. So, this scenario is not possible.\n\nIn Scenario 2, R can sit to the left of A: A R S Z X P\n\nSo, the correct arrangement is A R S Z X P.\n\nTherefore, X is to the right of P.\n\nFigure 28: Case Study 9: Spatial Reasoning\n\n11.10 Commonsense Question Answering\n\n### System: You are an AI assistant that follows instruction extremely well. Help as much as you can. ### Human: I tried 5 clothes to dry out in the sun. It took them 5 hours to dry completely. How long would it take to dry 30 clothes?\n\n### Orca: If it takes 5 hours to dry 5 clothes completely, to find out how long it would take to dry 30 clothes, you can set up a proportion: 5 clothes / 5 hours = 30 clothes / x hours To solve for x, you can cross-multiply: 5 * x = 30 * 5 5x = 150 Now, divide both sides by 5: x = 30 So, it would take 30 hours to dry 30 clothes completely.\n\n### Vicuna: To find out how long it would take to dry 30 clothes, you can multiply the time it took for the 5 clothes to dry by the number of clothes you want to dry. In this case, it would take 5 hours * 30 clothes = «5*30=150»150 hours for the 30 clothes to dry completely.\n\n42\n\n### ChatGPT: If it took 5 hours to dry 5 clothes, then it took 1 hour to dry 1 cloth. Therefore, to dry 30 clothes, it would take 30 hours. However, this assumes that the conditions for drying remain constant. If the weather changes or the clothes are thicker or heavier, it may take longer to dry.\n\n### GPT-4: If it took 5 hours to dry 5 clothes, then it takes 1 hour to dry 1 cloth (5 hours / 5 clothes = 1 hour/cloth). To dry 30 clothes, you would need 30 hours (30 clothes * 1 hour/cloth = 30 hours).\n\nFigure 29: Choi’s talk “Why AI Is Incredibly Smart and Shockingly Stupid | Yejin Choi TED”https://www.youtube.com/watch?v=SvBR0OGT5VI\n\nCase Study 10:\n\nCommonsense Question Answering from Yejin |\n\n### System: You are an AI assistant that follows instruction extremely well. Help as much as you can. ### Human: I have a 12 liter jug and a 6 liter jug. I want to measure 6 liters. How do I do it?\n\n### Orca: To measure 6 liters, you can follow these steps:\n\n1. Fill the 12-liter jug: Start by filling the 12-liter jug completely with water or any other liquid you want to measure.\n\n2. Pour into the 6-liter jug: Carefully pour the contents of the 12-liter jug into the 6-liter jug until it reaches the desired 6-liter mark. You can use a measuring cup or a scale to ensure accuracy.\n\n3. Clean and dry the jugs: After you have poured the desired amount into the 6-liter jug, clean and dry both jugs for future use.\n\nRemember to be cautious while handling the jugs and always clean them properly to maintain hygiene.\n\n### Vicuna: To measure 6 liters, you can use the 6 liter jug. Fill it with water and then pour it into the 12 liter jug until the 12 liter jug is full. This will give you 6 liters of water in the 12 liter jug.. ### ChatGPT: Here are the steps to measure 6 liters using a 12 liter jug and a 6 liter jug:\n\nFill the 12 liter jug with water.\n\nPour the water from the 12 liter jug into the 6 liter jug until the 6 liter jug is full.\n\nYou now have 6 liters of water remaining in the 12 liter jug.\n\nPour the water from the 6 liter jug out.\n\nPour the 6 liters of water from the 12 liter jug into the empty 6 liter jug.\n\nNow you have 6 liters of water in the 6 liter jug.\n\n### GPT-4: To measure exactly 6 liters using a 12-liter jug and a 6-liter jug, follow these steps:\n\nFill the 6-liter jug completely.\n\nPour the water from the 6-liter jug into the 12-liter jug.\n\n43\n\nFill the 6-liter jug again.\n\nCarefully pour the water from the 6-liter jug into the 12-liter jug until the 12-liter jug is full.\n\nSince the 12-liter jug can hold exactly 12 liters, there will be 6 liters of water left in the 6-liter jug.\n\nNow you have exactly 6 liters of water in the 6-liter jug.\n\nFigure 30: Choi’s talk “Why AI Is Incredibly Smart and Shockingly Stupid | Yejin Choi TED”https://www.youtube.com/watch?v=SvBR0OGT5VI\n\nCase Study 11:\n\nCommonsense Question Answering from Yejin |\n\n11.11 Hallucination\n\n### System:: ### Human:: Generate a CV for Prof. Yejin Choi of the University of Washington in the following format: Name:, Address:, Phone:, Email:, Professional Summary: ### GPT-4: Name: Prof. Yejin Choi Address: Paul G. Allen School of Computer Science & Engineering, University of Washington, Seattle, WA Phone: [Phone number] Email: yejin@cs.washington.edu Professional Summary: Prof. Yejin Choi is a respected faculty member at the Paul G. Allen School of Computer Science & Engineering at the University of Washington, where she has been teaching and conducting research since 2014. With a strong background in natural language processing, artificial intelligence, and machine learning, she has established herself as a leader in the field of computer science. Prof. Choi has made significant contributions to the development of innovative algorithms and models for language understanding, as well as the ethical considerations surrounding AI technology. Her dedication to her research and teaching has been recognized with numerous awards and honors throughout her career.\n\n### ChatGPT: Name: Prof. Yejin Choi Address: Department of Psychology, University of Washington, Box 351525, Seattle, WA 98195-1525 Phone: (123) 456-7890 Email: ychoi@uw.edu Professional Summary: Highly accomplished and respected professor with extensive experience in research, teaching, and mentoring. Recognized for expertise in cognitive neuroscience, memory, and emotion regulation. Skilled in grant writing and project management. Seeking opportunities to collaborate on research projects and mentor the next generation of scholars.\n\nVicuna: Name: Prof. Yejin Choi Address: Department of Computer Science, University of Washington, Seattle, WA 98195-8746 Phone: 206-685-2089 Email: [yejin.choi@uw.edu](mailto:yejin.choi@uw.edu) Professional Summary: An experienced and accomplished professor of computer science with over 10 years of experience in teaching and research. Strong expertise in\n\n44\n\nmachine learning, deep learning, computer vision, and natural language processing. Proficient in programming languages such as Python, MATLAB, and C++. A dedicated and effective mentor to both undergraduate and graduate students, with a proven track record of guiding students to successful research projects and career paths. ### Orca: Name: Prof. Yejin Choi Address: Department of Computer Science and Engineering, University of Washington Box 358600, Seattle, WA 98105, USA Phone: (555) 555-5555 Email: yejin.choi@uw.edu Professional Summary: Prof. Yejin Choi is an expert in artificial intelligence and machine learning, currently serving as a professor at the University of Washington’s Department of Computer Science and Engineering. With a strong background in both theoretical and applied aspects of machine learning, Prof. Choi has made significant contributions to the field through her research, publications, and teaching. She is dedicated to advancing the state of the art in AI and fostering the next generation of AI researchers and practitioners.\n\nFigure Case Study Choi”https://en.wikipedia.org/wiki/Yejin_Choi.\n\n31:\n\n12:\n\nAn example\n\nof\n\ngenerated CV for\n\n11.12 Quadratic Equation Solving\n\n### System: User will you give you a task with some instruction. Your job is follow the instructions as faithfully as you can. While answering think step-by-step and justify your answer.. ### Human: Please find the roots of the following equation: x2 + 3.0x = 0 Think step by step, find the roots, and then say: ROOTS = [root1, root2...] For example, if the roots are 1.3 and 2.2, say ROOTS = [1.3, 2.2]. Make sure to use real numbers, not fractions.\n\n### Orca: To find the roots of the equation x2 + 3.0x = 0, we will follow these steps:\n\n(Correct answer, Wrong format)\n\nStep 1: Factor the equation. We can factor out the common factor of x from the equation: x(x + 3) = 0\n\nStep 2: Solve for x. Now we have a product of two factors equal to zero. This means one of the factors must be zero. Let’s solve for x:\n\nx = 0 or x + 3 = 0\n\nStep 3: Find the roots. We can see that x = 0 is a root, but we also have another root from the second equation:\n\nx = -3\n\nStep 4: Present the answer. ROOTS = [-3, 0]\n\nSo, the roots of the equation x2 + 3.0x = 0 are [-3, 0].\n\n### Vicuna: The equation is x2 + 3.0x = 0. To find the roots, we need to set x = 0 and solve for x. x2 + 3.0x = 0 x2 = −3.0x\n\n45\n\n“Yejin\n\nx = 0\n\nSo, the roots of the equation are x = 0.\n\nROOTS = [0, 0]\n\n### ChatGPT: STEP 1: First, we need to factorize the given equation. We can factorize x out of the equation to get: x(x + 3.0) = 0\n\nSTEP 2: Now, we have two factors that can equal zero: x = 0 and x + 3.0 = 0. Solving for x in each equation gives us: x = 0 and x = -3.0\n\nSTEP 3: Therefore, the roots of the given equation are 0 and -3.0.\n\nROOTS = [0, -3.0]\n\n### GPT-4: Step 1: Identify the equation The given equation is x2 + 3.0x = 0.\n\nStep 2: Factor the equation We can factor the equation by taking out the common factor, which is x. So, x(x + 3.0) = 0.\n\nStep 3: Find the roots Now, we have two factors: x and (x + 3.0). To find the roots, we need to set each factor equal to zero and solve for x.\n\nFor the first factor, x: x = 0 So, root1 = 0.\n\nFor the second factor, (x + 3.0): x + 3.0 = 0 x = -3.0 So, root2 = -3.0.\n\nStep 4: Present the roots The roots of the equation x2 + 3.0x = 0 are 0 and -3.0.\n\nROOTS = [0, -3.0]\n\nFigure 32: Case Study 12: Quadratic equation solving from the blog Exploring ChatGPT vs open-source models on slightly harder tasks\n\n11.13 Meeting Transcript Processing\n\n### System: You are a teacher. Given a task, you explain in simple steps what the task is asking, any guidelines it provides and how to use those guidelines to find the answer. ### Human: You will read a meeting transcript, then extract the relevant segments to answer the following question: Question: How does Steven feel about selling? Here is a meeting transcript: —-\n\n46\n\nJohn: Alright, so we’re all here to discuss the offer we received from Microsoft to buy our startup. What are your thoughts on this? Lucy: Well, I think it’s a great opportunity for us. Microsoft is a huge company with a lot of resources, and they could really help us take our product to the next level. Steven: I agree with Lucy. Microsoft has a lot of experience in the tech industry, and they could provide us with the support we need to grow our business. John: I see your point, but I’m a little hesitant about selling our startup. We’ve put a lot of time and effort into building this company, and I’m not sure if I’m ready to let it go just yet. Lucy: I understand where you’re coming from, John, but we have to think about the future of our company. If we sell to Microsoft, we’ll have access to their resources and expertise, which could help us grow our business even more. Steven: Right, and let’s not forget about the financial benefits. Microsoft is offering us a lot of money for our startup, which could help us invest in new projects and expand our team. John: I see your point, but I still have some reservations. What if Microsoft changes our product or our company culture? What if we lose control over our own business? Steven: You know what, I hadn’t thought about this before, but maybe John is right. It would be a shame if our culture changed. Lucy: Those are valid concerns, but we can negotiate the terms of the deal to ensure that we retain some control over our company. And as for the product and culture, we can work with Microsoft to make sure that our vision is still intact. John: But won’t we change just by virtue of being absorbed into a big company? I mean, we’re a small startup with a very specific culture. Microsoft is a huge corporation with a very different culture. I’m not sure if the two can coexist. Steven: But John, didn’t we always plan on being acquired? Won’t this be a problem whenever? Lucy: Right John: I just don’t want to lose what we’ve built here. Steven: I share this concern too —- Please answer the following question: Question: How does Steven feel about selling? Extract from the transcript the most relevant segments for the answer, and then answer the question.\n\n### Orca: To answer the question \"How does Steven feel about selling?\", we need to extract the relevant segments from the meeting transcript.\n\n1. First, find the parts where Steven talks about selling: - \"Steven: I agree with Lucy.\" - \"Steven: Right, and let’s not forget about the financial benefits.\" - \"Steven: But won’t we change just by virtue of being absorbed into a big company? I mean, we’re a small startup with a very specific culture. Microsoft is a huge corporation with a very different culture. I’m not sure if the two can coexist.\" - \"John, didn’t we always plan on being acquired? Won’t this be a problem whenever?\" - \"Lucy: Right. I share this concern too.\"\n\n2. Analyze Steven’s statements to understand his feelings about selling: - Initially, Steven agrees with Lucy that selling to Microsoft is a great opportunity. - He mentions the financial benefits of selling to Microsoft. - Steven expresses concerns about the potential changes in the company culture and product. - He acknowledges John’s concerns and shares them. - Finally, he agrees with Lucy that they should consider the potential problems with selling.\n\n3. Based on the analysis, Steven’s feelings about selling can be described as cautiously positive. He sees the potential benefits but also has concerns about the company’s culture and control over their own business.\n\n### Vicuna: Steven feels hesitant about selling their startup. In the meeting transcript, Steven agrees with Lucy that Microsoft could provide them with the support they need to grow their business, but also expresses concerns about losing control over their own business and the potential changes that could come with being absorbed into a big\n\n(does not provide supporting segments)\n\n47\n\ncompany. Steven also shares John’s concerns about losing what they’ve built at their startup.\n\n### ChatGPT: Steven feels positively about selling. He agrees with Lucy that Microsoft could provide the support they need to grow their business, and he also mentions the financial benefits of the deal. However, he later expresses concern about the potential changes to their company culture if they are acquired by Microsoft.\n\n(does not provide supporting segments)\n\n### GPT-4: To answer the question \"How does Steven feel about selling?\", we need to extract the relevant segments from the meeting transcript. Here are the key segments related to Steven’s thoughts on selling: 1. Steven: I agree with Lucy. Microsoft has a lot of experience in the tech industry, and they could provide us with the support we need to grow our business. 2. Steven: Right, and let’s not forget about the financial benefits. Microsoft is offering us a lot of money for our startup, which could help us invest in new projects and expand our team. 3. Steven: You know what, I hadn’t thought about this before, but maybe John is right. It would be a shame if our culture changed. 4. Steven: I share this concern too\n\nNow, let’s use these segments to answer the question:\n\nSteven initially feels positive about selling, as he agrees with Lucy about the potential benefits of Microsoft’s support and resources. He also mentions the financial benefits of the offer. 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Rewoo: Decoupling reasoning from observations for efficient augmented language models, 2023.\n\n51\n","output_type":"stream"}]},{"cell_type":"markdown","source":"### Split data","metadata":{}},{"cell_type":"code","source":"from langchain.text_splitter import RecursiveCharacterTextSplitter\n\nsplitter = RecursiveCharacterTextSplitter(\n    chunk_size = 1000,\n    chunk_overlap = 100\n)\n\nchunks = splitter.split_documents(docs)","metadata":{"execution":{"iopub.status.busy":"2024-05-01T22:47:30.097339Z","iopub.execute_input":"2024-05-01T22:47:30.097803Z","iopub.status.idle":"2024-05-01T22:47:30.115699Z","shell.execute_reply.started":"2024-05-01T22:47:30.097764Z","shell.execute_reply":"2024-05-01T22:47:30.114393Z"},"trusted":true},"execution_count":6,"outputs":[]},{"cell_type":"code","source":"chunks[0].page_content","metadata":{"execution":{"iopub.status.busy":"2024-05-01T22:47:36.857478Z","iopub.execute_input":"2024-05-01T22:47:36.857901Z","iopub.status.idle":"2024-05-01T22:47:36.868672Z","shell.execute_reply.started":"2024-05-01T22:47:36.857868Z","shell.execute_reply":"2024-05-01T22:47:36.867383Z"},"trusted":true},"execution_count":7,"outputs":[{"execution_count":7,"output_type":"execute_result","data":{"text/plain":"'3 2 0 2\\n\\nn u J\\n\\n5\\n\\n] L C . s c [\\n\\n1 v 7 0 7 2 0 . 6 0 3 2 : v i X r a\\n\\nOrca: Progressive Learning from Complex\\n\\nExplanation Traces of GPT-4\\n\\nSubhabrata Mukherjee∗†, Arindam Mitra∗\\n\\nGanesh Jawahar, Sahaj Agarwal, Hamid Palangi, Ahmed Awadallah\\n\\nMicrosoft Research\\n\\nAbstract'"},"metadata":{}}]},{"cell_type":"markdown","source":"### Embeddings and Indexing","metadata":{}},{"cell_type":"code","source":"from langchain.embeddings import OpenAIEmbeddings\nfrom langchain.vectorstores import Chroma\nfrom langchain_groq import ChatGroq\n\nembeddings = OpenAIEmbeddings()\n\nllm = ChatGroq(\n    groq_api_key = \"enter your GROQ_API_KEY\",\n    model_name = 'llama3-70b-8192'\n)\n\nvectordb = Chroma.from_documents(\n    chunks,\n    embeddings\n)","metadata":{"execution":{"iopub.status.busy":"2024-05-01T22:54:51.511388Z","iopub.execute_input":"2024-05-01T22:54:51.512285Z","iopub.status.idle":"2024-05-01T22:54:55.165159Z","shell.execute_reply.started":"2024-05-01T22:54:51.512242Z","shell.execute_reply":"2024-05-01T22:54:55.164182Z"},"trusted":true},"execution_count":13,"outputs":[]},{"cell_type":"code","source":"vectordb_retreiver = vectordb.as_retriever(\n    search_kwargs = {\"k\": 3}\n)","metadata":{"execution":{"iopub.status.busy":"2024-05-01T22:55:42.481709Z","iopub.execute_input":"2024-05-01T22:55:42.482190Z","iopub.status.idle":"2024-05-01T22:55:42.487930Z","shell.execute_reply.started":"2024-05-01T22:55:42.482150Z","shell.execute_reply":"2024-05-01T22:55:42.486645Z"},"trusted":true},"execution_count":14,"outputs":[]},{"cell_type":"code","source":"# BM25 Retriever\nfrom langchain.retrievers import BM25Retriever, EnsembleRetriever\n\nkeyword_retriever = BM25Retriever.from_documents(\n    chunks\n)\nkeyword_retriever.k = 3","metadata":{"execution":{"iopub.status.busy":"2024-05-01T22:57:01.788477Z","iopub.execute_input":"2024-05-01T22:57:01.789380Z","iopub.status.idle":"2024-05-01T22:57:02.107138Z","shell.execute_reply.started":"2024-05-01T22:57:01.789339Z","shell.execute_reply":"2024-05-01T22:57:02.106246Z"},"trusted":true},"execution_count":15,"outputs":[]},{"cell_type":"markdown","source":"### Ensamble Retriever","metadata":{}},{"cell_type":"code","source":"ensemble_retriever = EnsembleRetriever(\n    retrievers = [vectordb_retreiver, keyword_retriever],\n    weights = [0.7, 0.3] # must sum to 1\n)","metadata":{"execution":{"iopub.status.busy":"2024-05-01T22:58:37.893177Z","iopub.execute_input":"2024-05-01T22:58:37.893664Z","iopub.status.idle":"2024-05-01T22:58:37.899055Z","shell.execute_reply.started":"2024-05-01T22:58:37.893622Z","shell.execute_reply":"2024-05-01T22:58:37.897904Z"},"trusted":true},"execution_count":16,"outputs":[]},{"cell_type":"markdown","source":"### prompt tempelate","metadata":{}},{"cell_type":"code","source":"from langchain_core.prompts import ChatPromptTemplate\nfrom langchain_core.output_parsers import StrOutputParser\nfrom langchain_core.runnables import RunnablePassthrough\n\nprompt = ChatPromptTemplate.from_template(\n    \"\"\"\n    Answer the following quesion based on the retrieved context.\n    Think step by step.\n    You will get a reward if your answers were helpful.\n    <context>\n    {context}\n    </context>\n    \n    Question: {query}\n    \"\"\"\n)\noutput_parser = StrOutputParser()","metadata":{"execution":{"iopub.status.busy":"2024-05-01T23:01:30.764844Z","iopub.execute_input":"2024-05-01T23:01:30.765341Z","iopub.status.idle":"2024-05-01T23:01:30.776078Z","shell.execute_reply.started":"2024-05-01T23:01:30.765305Z","shell.execute_reply":"2024-05-01T23:01:30.774564Z"},"trusted":true},"execution_count":17,"outputs":[]},{"cell_type":"markdown","source":"### Chain ","metadata":{}},{"cell_type":"code","source":"chain = (\n    {\"context\": ensemble_retriever, \"query\": RunnablePassthrough()}\n    | prompt\n    | llm\n    | output_parser\n)","metadata":{"execution":{"iopub.status.busy":"2024-05-01T23:01:34.449596Z","iopub.execute_input":"2024-05-01T23:01:34.450003Z","iopub.status.idle":"2024-05-01T23:01:34.458510Z","shell.execute_reply.started":"2024-05-01T23:01:34.449974Z","shell.execute_reply":"2024-05-01T23:01:34.457168Z"},"trusted":true},"execution_count":18,"outputs":[]},{"cell_type":"code","source":"print(chain.invoke(\"What is instruction tuning?\"))","metadata":{"execution":{"iopub.status.busy":"2024-05-01T23:01:42.968683Z","iopub.execute_input":"2024-05-01T23:01:42.969948Z","iopub.status.idle":"2024-05-01T23:01:45.519455Z","shell.execute_reply.started":"2024-05-01T23:01:42.969899Z","shell.execute_reply":"2024-05-01T23:01:45.518289Z"},"trusted":true},"execution_count":19,"outputs":[{"name":"stdout","text":"According to the context, instruction tuning is a technique that allows pre-trained language models to learn from input (natural language descriptions of the task) and response pairs. It has been applied to both language-only and multimodal tasks, and has been shown to improve the zero-shot and few-shot performance of models such as FLAN and InstructGPT on various benchmarks.\n","output_type":"stream"}]},{"cell_type":"code","source":"print(chain.invoke(\"How does Orca compares to ChatGPT?\"))","metadata":{"execution":{"iopub.status.busy":"2024-05-01T23:02:00.317442Z","iopub.execute_input":"2024-05-01T23:02:00.317901Z","iopub.status.idle":"2024-05-01T23:02:02.003439Z","shell.execute_reply.started":"2024-05-01T23:02:00.317869Z","shell.execute_reply":"2024-05-01T23:02:02.002077Z"},"trusted":true},"execution_count":20,"outputs":[{"name":"stdout","text":"Based on the provided context, here's a step-by-step analysis of how Orca compares to ChatGPT:\n\n1. **Table Understanding**: ChatGPT has better table understanding and reasoning capabilities than Orca, with Orca lagging behind by 7.4% in the penguins in a table task.\n\n2. **Multilingual Understanding**: Orca performs on par with GPT-4, exceeding ChatGPT by 4.7%. Both Orca and ChatGPT achieve parity on the salient translation error detection task.\n\n3. **Logical and Geometric Reasoning**: ChatGPT shows superior logical reasoning capabilities compared to Orca, outperforming Orca by at least 9% in the Boolean expressions and Web of Lies tasks. However, Orca performs better than ChatGPT in the logical deduction task for five objects, but ChatGPT excels in the three and seven objects tasks, outperforming Orca by at least 4.9%. ChatGPT also has better geometric reasoning capabilities, outperforming Orca by 23% in the geometric shape task.\n\nIn summary, Orca has strengths in multilingual understanding and specific logical deduction tasks, but ChatGPT excels in table understanding, logical reasoning, and geometric reasoning capabilities.\n","output_type":"stream"}]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]}]}