

RAFT: Adapting Language Model to Domain Specific RAG
6 snips Jun 28, 2024
Sai Kolasani, a researcher at UC Berkeley’s RISE Lab and Arize AI Intern, discusses RAFT, a method to adapt language models for domain-specific question-answering. RAFT improves models' reasoning by training them to ignore distractor documents, enhancing performance in specialized domains like PubMed and HotpotQA. The podcast explores RAFT's chain-of-thought-style response, data curation, and optimizing performance in domain-specific tasks.
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RAFT Analogy: Textbook and Exam
- Traditional fine-tuning is like a closed-book exam; RAG is like an open-book assessment.
- RAFT teaches the model how to use the "textbook" effectively during the "study phase."
Effective Context Utilization in RAG
- Focus on teaching LLMs to use context effectively in retrieval-augmented generation (RAG).
- This is the core of RAFT's effectiveness.
Robust RAG Systems
- Include distractor documents in your RAG system for improved robustness.
- This helps the model learn to discern relevant information.