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RAFT: Adapting Language Model to Domain Specific RAG

Deep Papers

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The Importance of Chain of Thought Reasoning and Data Curation in Language Models

The chapter delves into the significance of chain of thought reasoning in language models, highlighting its role in improving answer quality by navigating through mixed relevant and irrelevant information effectively. It explores the curation process in training datasets for domain-specific applications, emphasizing the need for human evaluation and high-quality data over data volume. The discussion includes insights on fine-tuning language models like Raft for efficient output and the use of synthetic data generation for training, showcasing examples of prompting models with reasoning chains for question-answering tasks.

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