RAFT: Adapting Language Model to Domain Specific RAG (AI summary)

Key Points

- The paper presents a new training strategy, called Retrieval Augmented Fine Tuning (RAFT), designed to enhance the ability of language models to answer questions within specific domains in "open-book" settings.

- RAFT focuses on fine-tuning language models for question-answering tasks based on a selected collection of documents, aiming to improve performance in domain-specific question-answering tasks.

- The paper proposes several crucial design decisions, including training the model with distractor documents, organizing the dataset so a portion lacks oracle documents in their context, and formulating answers in a chain-of-thought manner with direct quotations from relevant text.

- Evaluations on PubMed, HotpotQA, and Gorilla API Bench demonstrate RAFT's significant potential in enhancing language models' performance in in-domain Retrieval-Augmented Generation (RAG) tasks.

- The research addresses practical scenarios where language models are tasked with answering questions using domain-specific knowledge, and suggests that smaller, fine-tuned models are capable of performing comparably well in domain-specific question-answering tasks.

- The paper employs experiments to study the impact of RAFT on various benchmarks, comparing its performance against existing methods and demonstrating its effectiveness in improving language models' performance.

- RAFT's approach includes considerations for handling distractor documents during training to ensure the model is resilient to unhelpful information and robust in discerning and prioritizing relevant content.

- The research also explores the impact of the Chain-of-Thought approach in enhancing the model's performance, demonstrating that incorporating a reasoning chain significantly enhances training robustness.

- The paper provides an in-depth examination of the optimal proportion of training instances that should include oracle documents, revealing that mixing a fraction of data without the oracle document in its context is advantageous for in-domain RAG.

Summary

The paper "RAFT: Adapting Language Model to Domain Specific RAG" introduces Retrieval Augmented Fine Tuning (RAFT) as a method to improve large language models' (LLMs) ability to answer questions in "open-book" in-domain settings. RAFT integrates the model with a set of retrieved documents, training the model to ignore distractor documents and cite verbatim from the relevant document to answer the question, thus improving the model's reasoning ability. The paper presents findings that RAFT consistently improves the model's performance across PubMed, HotpotQA, and Gorilla datasets compared to traditional supervised fine-tuning, presenting a novel technique to improve pre-trained LLMs for in-domain Retrieval-Augmented Generation (RAG).

Impact of RAFT on Model's Performance and Evaluation of Training Approaches
The paper explores the impact of RAFT on the model's performance across specific datasets, providing a detailed comparison with existing methods, such as RAG and supervised fine-tuning. It presents datasets used in experiments to evaluate RAFT's impact on the model's performance, emphasizing performance gains across various benchmarks. Furthermore, the paper describes the impact of the number of distractor documents in RAFT on the model's performance, highlighting the need to train the model with context and striking a balance between relevant and irrelevant information. Additionally, the paper evaluates the necessity of training LLMs with the oracle context for RAG, showing that incorporating a portion of the training data without the oracle context may enhance the model's performance on RAG tasks.

Importance of Chain-of-Thought Approach and Test-Time Documents Quantity on Model's Performance
The paper also discusses the importance of integrating Chain-of-Thought approach to improve the model's performance, showing that adding Chain-of-Thought prompts significantly improves the model's accuracy. Additionally, it examines the impact of different quantities of test-time documents on the model's performance, demonstrating that training models with distractor documents makes the model more resilient to variations in the number of documents encountered during testing.
Overall Evaluation and Generalization of RAFT
Overall, the paper presents RAFT as a promising technique to enhance the performance of LLMs for domain-specific open-book exams and presents comprehensive evaluations and comparisons with existing methods, showcasing its potential to improve LLMs' abilities in in-domain RAG. The paper also discusses the generalization of RAFT to a variable number of test-time documents, as well as relevant literature and prior research in the field of language model fine-tuning.

Insights into LLMs Refinement and Future Research Opportunities
The paper offers valuable insights into the refinement of LLMs and emphasizes the importance of context comprehension in training datasets to enhance the model's capability to process text effectively. It concludes by anticipating further interest in in-domain RAG and the potential for smaller, fine-tuned models to perform well in domain-specific question-answering tasks, laying the groundwork for future research and applications.

Reference: https://arxiv.org/abs/2403.10131