总结Prompt&LLM论文,开源数据&模型,AIGC应用
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Updated
May 23, 2024
总结Prompt&LLM论文,开源数据&模型,AIGC应用
Awesome resources for in-context learning and prompt engineering: Mastery of the LLMs such as ChatGPT, GPT-3, and FlanT5, with up-to-date and cutting-edge updates.
[EMNLP 2023, Findings] GRACE: Discriminator-Guided Chain-of-Thought Reasoning
✨✨Latest Papers and Datasets on Multimodal Large Language Models, and Their Evaluation.
The official GitHub page for the survey paper "A Survey of Large Language Models".
This repository highlights the LLMs reasoning capabilities of ✨ Mistral / LLaMA-3 / Phi-3 / Gemma / Flan-T5 / GPT-4o ✨ in Targeted Sentiment Analysis in Russian / Translated to English mass-media 📊
DriveLM: Driving with Graph Visual Question Answering
Reasoning in Large Language Models: Papers and Resources, including Chain-of-Thought, Instruction-Tuning and Multimodality.
Codes for ICML 2024 paper: "Video-of-Thought: Step-by-Step Video Reasoning from Perception to Cognition"
Awesome deliberative prompting: How to ask LLMs to produce reliable reasoning and make reason-responsive decisions.
A framework for evaluating the effectiveness of chain-of-thought reasoning in language models.
This repository contains the code snippets used in "LLM Prompt Engineering For Developers"
An Easy-to-use Instruction Processing Framework for LLMs.
The official repo for “TextCoT: Zoom In for Enhanced Multimodal Text-Rich Image Understanding”.
Awesome-LLM-Robustness: a curated list of Uncertainty, Reliability and Robustness in Large Language Models
ragTAG is a conversational AI script that creates a roundtable dialogue between user assigned characters with their own different objectives and perspectives.
Repository for Interleaving Retrieval with Chain-of-Thought Reasoning for Knowledge-Intensive Multi-Step Questions, ACL23
Speak It Out: Solving Symbol-Related Problems with Symbol-to-Language Conversion for Language Models
Official implementation for "Automatic Chain of Thought Prompting in Large Language Models" (stay tuned & more will be updated)
Official implementation of LoT paper: "Enhancing Zero-Shot Chain-of-Thought Reasoning in Large Language Models through Logic"
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