Core Concepts
RAFT improves language models for in-domain question answering by training them to ignore distractor documents and focus on relevant information.
Abstract
RAFT introduces a novel training recipe that enhances language models' ability to answer questions in an "open-book" setting within specific domains. By incorporating Retrieval Augmented Fine Tuning, the model learns to filter out distractor documents and extract key information from relevant sources. This approach improves the model's performance on various datasets like PubMed, HotpotQA, and Gorilla API Bench. RAFT combines supervised fine-tuning with retrieval augmented generation, enabling models to reason effectively and provide accurate responses based on domain-specific knowledge.
Stats
RAFT consistently outperforms Supervised-finetuning both with- and without- RAG across PubMed (Dernoncourt & Lee, 2017), HotpotQA (Yang et al., 2018), and HuggingFace Hub, Torch Hub, and Tensorflow Hub Gorilla datasets (Patil et al., 2023).
RAFT does much better on tasks like HotpotQA and HuggingFace datasets (30.87% on HotpotQA and 31.41% on HuggingFace).
Compared with DSF on the specific dataset, our model does better at relying on the provided context to solve the problem.
Quotes
"RAFT aims to not only enable models to learn domain-specific knowledge through fine-tuning but also ensure robustness against inaccurate retrievals."
"In RAFT, we train the model to answer the question from Document(s) to generate an answer."
"RAFT consistently outperforms the baselines across various specialized domains."