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Performing Prompt engineering on a dialogue summarization task using Flan-T5 and the dialogsum dataset. Exploring how different prompts affect the output of the model, and compare zero-shot and few-shot inferences.
Discussed about 4 use-cases or case studies. Discussed about the approaches and significance of these use-cases as these are different from others. There are several approaches available which can be done using LLM but here the approaches and it's significance could bring insightful approaches towards it's execution.
This project is based on fine-tuning LLM models (FLAN-T5) for text summarisation task using PEFT approach. All evaluation metrics being computed on ROUGE scoring and LoRA optimisation techniques being used for fine-tuning.
Text-To-Text Textbots to Demonstrate Output Differences Between Models Trained on Filtered/Unfiltered Datasets for HSS4 - The Modern Context: Select Figures and Topics
Dialogue Summary LLM - FLAN - T5: An implementation of the Flan-t5 LLM to summarize dialogues. Prompt Engineering , Fine tuning with PEFT and fine tuning with RL (PPO) is explored within this project.