InstructRAG: Instructing Retrieval-Augmented Generation via Self-Synthesized Rationales
Large language model;retrieval-augmented generation
Yes
No ethics review needed.
The questions can be viewed in the weakness
1. InstructRAG is a simple but effective method to denoise retrvied information and justify its predicted final answers by generating denoising reponse 
2. InstructRAG does not require any additional supervision and can be applied to both in-context learning and supervised fine-tuning settings 
3. good presentation and  detailed experiment
InstructRAG introduct one interesting method to explicitly learn the denoising process through self-synthesized rationales.  InstructRAG requires no additional supervision,  allows for easier verification of the predicted answers, and effectively improves generation accuracy.
1. The rationale data generatioin is crutial in InstructRAG.  I hope to get more details about how to denoise wrong rationales and how to ensure the generated rationales satisfy the corresponding standard. 
2. I have some questions about how to use rationale in evaluation. From what I understand, InstructRAG needs to generate rationales first,  then concats the retrvied information and generated rationales to LM.  Maybe the added computation cost need to be analysized in the paper.
Yes
No ethics review needed.
The paper presents an interesting approach to generating rationales for document retrieval tasks. However, there are several aspects that require further clarification and improvement to strengthen the validity and robustness of the proposed method. Below are my detailed comments:
1. It is unclear whether the document collections used in the experiments contain noise samples, and if so, what the concentration of these noise samples is. Understanding the presence and extent of noise is crucial for evaluating the robustness of the proposed method.
2. Beyond validating the effectiveness of the rationales through final experimental outcomes, how was the intrinsic quality of the generated rationales assessed? What measures were taken to ensure that the model-generated rationales are free from noise and that the selected documents are indeed highly relevant? This is important to confirm that the rationales are not only effective but also reliable and interpretable.
The paper effectively highlights the importance of denoising in RAG systems. This is a significant contribution to the field, as it addresses a critical issue that can significantly impact the performance of such models. The authors provide a thorough set of experiments that convincingly demonstrate the effectiveness of their proposed method. The results show that INSTRUCTRAG consistently outperforms both training-free baselines and training baselines across various metrics, which strongly supports the claim that self-synthesized denoising rationales are effective. Besides, the paper is well-organized and clearly written, making it easy for readers to follow the methodology, experimental setup, and results. The figures and tables are well-designed and enhance the understanding of the findings.
This paper introduces INSTRUCTRAG, a novel approach to enhance retrieval-augmented generation by explicitly teaching language models to denoise retrieved information. Unlike traditional RAG methods that implicitly handle noisy inputs, INSTRUCTRAG leverages the instruction-following capabilities of LMs to generate synthetic rationales explaining how the correct answer is derived from the retrieved documents. These synthetic rationales serve as explicit denoising supervision, which can be used for in-context learning or supervised fine-tuning. This method not only improves the quality of in-domain RAG tasks but also enhances out-of-domain generalization. Finally, the paper demonstrates that by utilizing self-synthesized rationales, INSTRUCTRAG effectively addresses the challenges posed by noisy retrievals, making it a promising advancement in the field of RAG.
The paper could benefit from a more detailed description of the document retrieval process. Specifically, it is unclear whether the document collections used in the experiments contain noise samples, and if so, what the concentration of these noise samples is. This information is crucial for understanding the robustness of the proposed method and its applicability to real-world scenarios where noise data is often present. In addition, this paper could be strengthened by including a comparative analysis with other denoising techniques that have been proposed in the literature. This would help to contextualize the performance of INSTRUCTRAG and highlight its unique contributions.
Yes
No ethics review needed.
A detailed analysis of the noise present in the dataset would be valuable for better understanding the robustness of the proposed method. More analysis of examples and rationales will be helpful. Testing it across different base models would be beneficial in assessing the robustness and generalizability of the proposed method.
1. The method effectively leverages existing data, reducing the need for costly new annotations, which often involve extensive human effort.

2. Demonstrating consistent outperformance over traditional RAG methods across multiple benchmarks indicates effectiveness.
The paper introduces an instructed RAG method designed to explicitly learn the denoising process in language models using self-synthesized rationales. InstructRAG addresses these issues by instructing LMs to generate explanations on how ground-truth answers are derived from retrieved documents. These explanations are rationales utilized in two ways: in-context learning to teach explicit denoising without additional data and as data for supervised fine-tuning. This approach does not require extra human-labeled supervision, simplifies the verification of predictions, and improves generation accuracy.
1. The paper introduces a method that may involve noise in extracted knowledge or filtering information. However, the extent and nature of this noise are not adequately discussed. A quantitative measurement of the noise and its impact on the model’s performance would provide valuable insights into the robustness of the proposed method. Analyzing and quantifying the noise would also help clarify how much of the model’s learning is genuinely informative versus potentially spurious.

2. The paper would benefit from a more in-depth qualitative analysis of the extracted knowledge, including illustrative examples and rationales. Conducting a human study to evaluate whether the extracted knowledge aligns with useful, meaningful content (or if it simply reinforces dataset biases) would add a layer of rigor. This evaluation could help establish whether the proposed method provides actionable insights or merely reflects the underlying biases in the dataset. 

3. The proposed method’s restriction on accessing additional information sources (GPTs), relying primarily on self-regularization, may limit its practical utility in real-world applications where additional context and supervision are often available. Furthermore, the filtering and re-ranking mechanisms can be viewed as forms of self-regularization, and evaluating the effects of incorporating or comparing these elements would provide a more balanced assessment. Testing the method with and without these components could illustrate its practical utility in more realistic scenarios.

4. The paper’s approach, which employs instruction tuning, in-context learning, and knowledge extraction, aligns closely with established methodologies in related fields. As a result, the method may lack significant novelty, as it primarily adapts a classical pipeline to the Retrieval-Augmented Generation (RAG) domain. Introducing new techniques or unique modifications tailored to RAG-specific challenges could significantly enhance the paper’s contribution to the field.

5. The paper would be strengthened by testing the proposed rationale learner across various models to assess its robustness. Demonstrating consistent performance improvements with different model architectures would reinforce the generalizability of the method and provide a more comprehensive evaluation of its effectiveness.
Yes
No ethics review needed.
1. If the content in the documents does not explicitly contain the answer to the question, but the answer can still be inferred from the document content, can the rationale generator correctly reason and identify the relevant content? Did the authors conduct any corresponding ablation experiments or case studies to prove this?
1. This paper proposes a RAG (Retrieval-Augmented Generation) paradigm that is suitable for both fine-tuning and in-context learning (ICL) and demonstrates its effectiveness. The design and validation of the entire approach are interesting.
2. This paper uses ablation experiments to prove that providing true answers and retrieved documents is very important for inference generation.
3. By comparing ICL and FT (fine-tuning) methods, the authors found that example-based reasoning should only be applied to InstructRAG-ICL, as it negatively impacts the performance of InstructRAG-FT. They provide reasoning for this, which is valuable for guiding future research.
4. Through out-of-distribution (OOD) experiments, the authors demonstrate that both InstructRAG-ICL and InstructRAG-FT can generalize well to unseen tasks.
This paper introduces InstructRAG, a model that learns denoising through self-generated rationales, which can be applied as in-context learning (ICL) demonstrations or supervised fine-tuning (SFT) training data to enhance performance. Experimental results across diverse datasets confirm the method’s effectiveness.
1. If the complete answer to a question is distributed across multiple documents, with each document containing only part of the answer, this approach might mistakenly filter out these useful documents, leading to hallucinations or an inability to correctly answer the question. Could the authors provide more detailed analyses and experiments to clarify or verify the model's robustness?
2. The authors conducted experiments using only LLaMA-3-Instruct as the backbone, with the quality of self-synthesized rationales largely influenced by the underlying model's capability. Low-quality rationales may impair model performance, suggesting that the success of this approach could be attributed to LLaMA-3-Instruct's strong capabilities. Could the authors provide additional experiments with other backbones, such as LLaMA-2, Qwen, and Mistral, to help validate the method’s transferability and generalizability?
3. Although the datasets used in this paper include a variety of question-answering formats, such as short-form, multi-hop, and long-form, they are all based on wiki-style content. To more convincingly demonstrate the method's effectiveness, the authors should further extend their evaluation to more realistic and domain-diverse RAG datasets, like FreshQA and BRIGHT. Could the author also discuss the potential challenges in applying their method to these datasets and propose specific experiments to show the performance?
4. In Section 3.1, the experimental setting is unclear, do the authors utilize all the training samples for each dataset to generate rationales? Why do the authors adopt different retrievers for different datasets? What are the performance differences of the retrievers, such as Contriever, DPR, GTR, and BM25? Why is 2WikiMultiHopQA required to recall 10 documents while other datasets only require 5 documents?
Yes
No ethics review needed.
Please refer to the Summary section.
Please refer to the Summary section.
The article introduces a simple yet effective RAG method to address noise problems in retrieved text results. Although the technique appears straightforward, I believe its simplicity enhances its versatility. I summarize the advantages as follows:

1. The approach is well-founded, as noise problems are prevalent in real-world RAG scenarios. InstructionRAG effectively addresses this problem.
2. The design is sound. The first phase generates rationales, which mitigate potential noise issues. Furthermore, the authors note, "The consistency ratio on training samples with at least one relevant document containing the ground-truth answer is 98% on average across five benchmarks, supporting the reliability of synthetic rationales as a sanity check." This aspect is crucial to the method's success.
3. The experiments are comprehensive, with validation across five datasets, demonstrating the effectiveness of InstructionRAG.

However, one might question whether using such sampling methods to address noise issues is worthwhile. This approach incurs additional time costs, which I believe cannot be overlooked. Exploring whether enhancing retrieval models might more effectively resolve this problem is a worthwhile consideration.

Overall, I am quite satisfied with the InstructionRAG method, which represents an influential contribution to the RAG field.
Please refer to the Summary section.
Instructing LLMs to explicitly denoise retrieved contents through self-synthesized rationales for better accuracy and trustworthiness in RAG.
@inproceedings{
anonymous2024instructrag,
title={Instruct{RAG}: Instructing Retrieval-Augmented Generation via Self-Synthesized Rationales},
author={Anonymous},
booktitle={Submitted to The Thirteenth International Conference on Learning Representations},
year={2024},
url={https://openreview.net/forum?id=P1qhkp8gQT},
note={under review}
}
Retrieval-augmented generation (RAG) has shown promising potential to enhance the accuracy and factuality of language models (LMs). However, imperfect retrievers or noisy corpora can introduce misleading or even erroneous information to the retrieved contents, posing a significant challenge to the generation quality. Existing RAG methods typically address this challenge by directly predicting final answers despite potentially noisy inputs, resulting in an implicit denoising process that is difficult to interpret and verify. On the other hand, the acquisition of explicit denoising supervision is often costly, involving significant human efforts. In this work, we propose InstructRAG, where LMs explicitly learn the denoising process through self-synthesized rationales --- First, we instruct the LM to explain how the ground-truth answer is derived from retrieved documents. Then, these rationales can be used either as demonstrations for in-context learning of explicit denoising or as supervised fine-tuning data to train the model. Compared to standard RAG approaches, InstructRAG requires no additional supervision, allows for easier verification of the predicted answers, and effectively improves generation accuracy. Experiments show InstructRAG consistently outperforms existing RAG methods in both training-free and trainable scenarios, achieving a relative improvement of 8.3% over the best baseline method on average across five knowledge-intensive benchmarks. Extensive analysis indicates that InstructRAG scales well with increased numbers of retrieved documents and consistently exhibits robust denoising ability even in out-of-domain datasets, demonstrating strong generalizability.
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Large language model retrieval-augmented generation
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InstructRAG: Instructing Retrieval-Augmented Generation via Self-Synthesized Rationales
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