RAFT: Adapting Language Model to Domain Specific RAG

Summary

This paper introduces RAFT to improve LLM's RAG performance. It does this by adding distractive documents and chain-of-thought reasoning during fine-tuning on open-book QA datasets. Including distractive documents helps the LLM focus on important info and ignore unimportant details in the top-k documents. Also, adding chain-of-thought reasoning makes its responses more accurate.

Rating

6

Confidence

4

Ethics flag

1

Reasons to accept

The two methods proposed in the paper, namely, incorporating distractive documents into the context and implementing reasoning chains in the models, are intuitive and have demonstrated significant enhancements in the model's performance across various benchmarks. The paper provides thorough experiments to support its assertions.

Reasons to reject

1. Applying Chain-of-Thought (CoT) to all the fine-tuning tasks may introduce limitations. While CoT is known to be helpful for challenging questions, it might not be suitable for simpler tasks, such as sentiment analysis. I notice that in Table 1 and Table 2, the authors only present main experiments and ablations on tasks originally designed as explainable tasks (i.e., tasks requiring CoT). However, what about the main results and ablations for NQ and Trivia QA, which were not originally presented in the CoT mode? Also, Table 1 exceeds the page width. 2. The evaluation of domain-specific knowledge is not sufficient. While the author proposes that RAFT can inject domain-specific knowledge into the LLM, it's essential to understand if the fine-tuned LLM can effectively apply its acquired domain knowledge from seen datasets to unseen datasets within the same domain. For example, consider PubmedQA, which enriches the LLM with biomedical knowledge. During testing, can the fine-tuned model perform well on task types not directly related to PubmedQA but could still benefit from the domain knowledge learned from the PubmedQA dataset?

Questions to authors

1. Regarding Chain-of-Thought, I'm curious about whether fine-tuning using CoT would affect the performance of tasks not originally designed as CoT. For instance, what about the ablations on NQ and TQA? I suspect that the fine-tuned model might initially generate some CoT before producing the final answer. Therefore, I'm interested in whether the final answer aligns with the ground truth. 2. Can the domain-specific knowledge learned from the trained dataset be applied to other types of tasks? For instance, could a model trained on PubmedQA also perform well on benchmarks like MedMCQA [1] or Bio-MMLU [2] also in the biomedicine domain? As suggested in [3], models trained on domain-specific reading comprehension corpora (i.e., a form of open-book QA) can apply domain-specific knowledge to other tasks within the same domain, which is similar to your fine-tuning setting. [1] MedMCQA: A Large-scale Multi-Subject Multi-Choice Dataset for Medical domain Question Answering [2] Measuring Massive Multitask Language Understanding [3] Adapting Large Language Models via Reading Comprehension

Summary

This paper proposes Retrieval Augmented Fine Tuning (RAFT), a fine-tuning method leveraging RAG in domain-specific question-answering tasks. The main idea is to fine-tune LLMs on target domain data with a combination of oracle document context and distractor document context. Comparing with raw LLM, LLM with RAG, and Finetuned LLM, RAFT significantly performs the baselines. Analysis on the fine-tuning data is shown to demonstrate how RAFT works.

Rating

6

Confidence

5

Ethics flag

1

Reasons to accept

1. The proposed method achieves strong performance compared to baselines. 2. Useful results to prepare domain-specific fine-tuning data in a specific domain, including various baselines and datasets.

Reasons to reject

The idea is relatively straightforward and lacks novel contribution. Domain-specific fine-tuning and RAG are widely known to be effective to solve domain-specific tasks. Both of them are ways to leverage the domain training data. Thus, the results are mostly expected in this paper. Compared to baselines, the proposed method has the following advantages, retrieval and domain-specific annotation: 1. Retrieval is an essential step in the open domain QA task and DSF without proper prompting is a meaningless baseline. DSF with question-answer pairs could only lead to memorization of answers for LLMs since there is no clue how these answers can be generated from the input. 2. Domain-specific annotation is also important. Even though the LLMs can generalize well on most common knowledge, they still struggle on domain-specific problems and need the downstream task's annotation. Thus, comparing RAFT to baselined without domain-specific fine-tuning, the performance gain is also expected. Thus, combining these two components is not a novel work.

Questions to authors

One possible way to justify RAFT is effective is to apply it to tasks other than open-domain QA, i.e. when the domain-specific annotation is unknown, or when the retrieval is not an essential step. Do you consider trying tasks such as text-to-SQL or reasoning-based QA?

Summary

The authors introduce retrieval-augmented finetuning (RAFT), which trains LLMs to be more robust to retrieval errors in open-domain QA by including distractor documents during instruction fine-tuning. This is achieved by incorporating training examples with mixtures of oracle and distractor documents and without oracle documents entirely, as well as the full reasoning chain to identifying the answer. The authors demonstrate consistent performance improvements over standard RAG and finetuning on PubMed, HotpotQA, and Gorilla APIBench.

Rating

9

Confidence

4

Ethics flag

1

Reasons to accept

- The authors introduce and demonstrate RAFT as an effective training strategy for RAG-style tasks. Introducing distractor documents during instruction fine-tuning improves these models' ability at inference time. - There is a clear hypothesis, good experimental design, and reasonable ablations - Results are provided over a good range of datasets and demonstrate the effectiveness of the technique - The paper is well written and organized

Reasons to reject

No obvious reasons

Summary

This paper presents RAFT, a fine-tuning method designed to enhance the performance of retrieval-augmented generation (RAG) for language models in open-book, in-domain question answering scenarios. The approach involves training language models using a mix of oracle and distractor documents, enabling them to discern relevant information from less useful content in the top-k retrieved documents. By comparing their method against conventional RAG baselines, the authors demonstrate that their approach significantly outperforms existing methods across five question answering datasets.

Rating

7

Confidence

5

Ethics flag

1

Reasons to accept

[1] The RAFT approach is not only straightforward to implement but also consistently outperforms baseline models by a significant margin across a variety of domain-specific question answering datasets in the open-book setting. [2] The paper is well-organized and includes figures and examples that make the central concepts more intuitive and easier to understand. [3] In addition to using oracle documents during training, the authors effectively incorporate distractor documents and systematically demonstrate their impact on model performance.

Reasons to reject

[1] Data Usage Inconsistency: The authors seem to focus on domain-specific datasets such as PubMed and Huggingface, as indicated in the title. However, they have not explicitly stated the inclusion of general domain datasets like NQ and TQA in Section 5. This data usage inconsistency may lead readers to question the analysis of "domain-specific" settings. [2] Term Inconsistency: There are several key terms used inconsistently throughout the paper, which could reduce readability and clarity. The inconsistencies include: Is it 'Retrieval Augmented Fine Tuning' or 'Retrieval Aware Fine-Tuning'? Is it 'golden documents' or 'oracle documents'? Is it 'sampled negative documents' or 'distractor documents'? [3] Missing Implementation Details: The paper lacks detailed information on the choice of hyperparameters used in the main experiment (Table 2). This absence of details may hinder readers from reproducing the results presented in the paper.

Reply to authors

Dear Authors, **Regarding the CoT Setting** I hope you can show the results for NQ and TQA because running an inference and reporting the results does not take too much time, as far as I know. Moreover, from Figure 6 in your paper, you've already done some experiments on NQ and TQA, which suggests it is not too hard for you to run and report these results. **Regarding Domain Knowledge** Cross-document evaluation (where the documents are still from the same dataset) may not be enough to demonstrate that the model has gained generalizable domain knowledge. I highly recommend adding at least one cross-dataset experiment to strengthen your claims.

Author Response

We would like to thank the reviewer for spending time replying to us. We have some new results below: **CoT Setting**: We ran some additional ablation on the CoT for NQ and TQA and reported the results below. As the reviewer mentioned, we see a slight degradation of CoT compared to non-CoT settings (but roughly the same number). This might be because the TQA and NQ tasks focus on citing the answering text from the context above without much reasoning involved. We want to say that given the results are very similar, adding CoT is still in general helpful in the settings where the reasoning is heavily involved. However, this indeed is an interesting finding, we will clarify this in the paper and add a discussion on this. NQ: CoT: 49.10%; Non-CoT: 50.90% TQA: CoT: 62.77%; Non-CoT: 64.77% **Domain Knowledge** We managed to run some ablation on how the knowledge can be generalized between datasets. We have three anonymous figures here. In this setting, we train on NQ/TQA/Hotpot and test on News QA/Squad QA/Web Questions. We found that RAFT can indeed generalize to new domains as well. In the figure, we mainly study how the percentage of the golden documents ($p$%) included in the training dataset will affect the generalization performance. The curves demonstrate that domain generalization can mostly benefit from a training dataset of 100% golden documents, as remembering the knowledge in one domain won't necessarily help another. https://drive.google.com/file/d/1R30PcDaMeMyady3NZ6uFj-vlnHYron6i/view?usp=sharing https://drive.google.com/file/d/1dckLBNICJL4YCDvkyoYc4euQFCHGtgGD/view?usp=sharing https://drive.google.com/file/d/1bM860jzMlwmt9UqWgTEO7wam0fkxoK7A/view?usp=sharing