Community Paper Reading: RAFT - Adapting Language Model to Domain Specific RAG

​We’re excited to host Sai Kolasani, researcher at UC Berkeley’s RISE Lab, to talk about his work on RAFT: Adapting Language Model to Domain Specific RAG. RAFT is a training recipe that improves an LLM’s ability to answer questions in a “open-book” in-domain settings. Given a question, and a set of retrieved documents, the model is trained to ignore documents that don’t help in answering the question (aka distractor documents). This coupled with RAFT’s chain-of-thought-style response, helps improve the model’s ability to reason. In domain-specific RAG, RAFT consistently improves the model’s performance across PubMed, HotpotQA, and Gorilla datasets, presenting a post-training recipe to improve pre-trained LLMs to in-domain RAG.