InstructRAG: Instructing Retrieval-Augmented Generation via Self-Synthesized Rationales
Authors: Zhepei Wei
Wei-Lin Chen
Yu Meng
Venue: ICLR 2025 Poster (2025)
Keywords: Large language model
retrieval-augmented generation
Primary area: foundation or frontier models, including LLMs
TL;DR: Instructing LLMs to explicitly denoise retrieved contents through self-synthesized rationales for better accuracy and trustworthiness in RAG.

Abstract: 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.

Published as a conference paper at ICLR 2025
INSTRUCTRAG: INSTRUCTING RETRIEVAL AUG- MENTED GENERATION VIA SELF-SYNTHESIZED RA- TIONALES
Zhepei Wei Wei-Lin Chen Yu Meng
Department of Computer Science
University of Virginia (zhepei wei, wl wlchen, yuwengg)ewirrginia..c
ABSTRACT
autrivval-augment generation (RAG) has shown promising potential enhance the accuracy and factuality of language models (LMs). However, imperfect re- trievers or noisy corpora can introduce misleading or even erroneous information to the retrieved contents, posing significant challenge to the generation qual- ity. Existing RAG methods typically address this challenge by directly predicting final answers despite potentially noisy inputs, resulting in implicit denoising process that is difficult to interpret and verify. On the other hand, the acquisi- tion 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 derived from retrieved documents. Then, these rationales can be used either demonstrations for in-context learning of explicit denoising or as supervised fine-tuning data to train the model. Com- pared standard RAG approaches, INSTRUCTRAG requires no additional su- pervision, 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 av- erage across five knowledge-intensse 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.
INTRODUCTION
While large language models (LMs) have demonstrated remarkable text generation abilities (Brown et al., 2020; Team et al., 2023; Touvron et al., 2023), they may occasionally produce factually in- correct contents (Dhuliawala et al., 2023; Huang et al., 2023a; et al., 2023; Sun et al., 2023; Xu et al., 2024d; Zhang et al., 2023), particularly when the task at hand requires the most current in- formation or oft-of-dommin knowledge not adequately represented in the pre-training corpus (Jiang et al., 2023b; Shuster et al., 2021; Yu et al., 2023; Zhao al., 2023). This limitation significantly hinders the reliable deployment of LMs in high-stakes domains where factuality is crucial (Magesh al., 2024; Singhal et al., 2023; Xiao al., 2021; Xiong et al., 2024).
In light of this, atrieval-auggmente generation (RAG) (Asai et al., 2023b; Guu et al., 2020; Izacard al., 2023; Khandelwal et al., 2019; Lewis et al., 2020) has been introduced to enhance the gener- ation accuracy of LMs in nowledge-intensive tasks by leveraging the most up-to-date information and specialized knowledge from external sources (Kasai al., 2024; Vu et al., 2023; Yang et al., 2024: Zhou et al.. 2022). However, the retrieved contents are typically mixed with irrelevant or even erroneous information due the absence of perfect retrieval solutions (Izacard et al., 2021; Karpukhin et al., 2020; Khattab et al., 2022; 2023; Shi et al., 2023; Su et al., 2024) and the presence
Code available https: //gi thub. com wei zhepei ruCtRAG.

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Published as a conference paper at ICLR 2025
Vanilla RAG
InstructRAG
Retrieved documents
Retrieved documents
Question ff
Question g
Learning Objective
Leamning Objective
do
Po (nlg, di.... dg
Doc d3
Polrig, di..., dx}
Language Model
Answer
Doo d/2
Language Model MMp
Rationale
Mg
Input: cretreved documentss Bazed inownodge provicted information, question equesstions
Input:
HAGS
Doc di.
Doo di
Output: The second document mentions.... More of dincuments 0rD rolavent... Therofovs, the answer sanswers
Outputt: sanswers
Figure 1: Comparison between vanilla RAG and our INSTRUCTRAG, In vanilla RAG, the model is tasked to directly predict answers given user queries and potentially noisy retrieved documents, without explicit denoising processes explanations for how the answer is derived. In contrast, our proposed INSTRUCTRAG generates rationales that explicitly denoise the retrieved documents and justify the predicted answers, enhancing both the generation accuracy and trustworthiness.
of noisy data in the retrieval corpus (Izacard Grave, 2021; Li et al., 2023; Yoran et al., 2024), posing a long-standing challenge to almost all RAG systems. Typically, vanilla RAG approaches address this issue implicitly by training LMs to directly predict correct answers despite noisy inputs. Such latent processes are not only difficult to interpret and verify but also vulnerable to higher noise ratios, especially when the number of retrieved documents is large (Chen et al., 2024: Cuconasu et al., 2024; Liu et al., 2024a; Wu et al., 2024). On the other hand, obtaining high-quality explicit denoising supervision often requires substantial human efforts, which is time-consuming and costly.
In this work, we introduce new RAG framework, INSTRUCTRAG, which enables the LM plicitly denoise retrieved information and justify its predicted final answers by generating denois- ing responses (i.e., rationales), as illustrated in Figure Compared to vanilla RAG approaches, INSTRUCTRAG does not require any additional supervision, while enjoying improved generation accuracy and trustworthiness. Specifically, our method consists of two steps. First, given a set of question- question-ansser answer pairs and potentially noisy retrieved documents, we prompt an instruction-tume LM to synthesize denoising rationales that analyze the documents and articulate how they lead the ground-truth answers ($ 2.2). Then, these synthetic rationales can be utilized as in-context learning examples or as supervised fine-tuning data, allowing the LM to explicitly learn to denoise retrieved contents ($ 2.3). The effectiveness of INSTRUCTRAG can be attributed to the strong instruction- following ability of LMs (Jiang et al., 2024b; Ouyang et al., 2022; Wei et al., 2021), a significant feature that still remains underexplored in the context of RAG. We show that such selt-synthesized rationales not only provide high-quality explicit denoising supervision for in-domain RAG tasks, but also facilitate superior out-of-domain generalization. This finding underscores how instruction- tuned LMs can synthesize generalizable supervision to overcome the inevitable noise in RAG.
The main contributions of this work are follows: (1) We propose INSTRUCTRAG, simple yet effective RAG framework that allows LMs to explicitly denoise retrieved contents by generating rationales for better verifiability and trustworthiness. (2) INSTRUCTRAG is a self-synthesis method that does not require additional supervision compared to standard RAG methods, and can be seam- lessly applied both in-context learning and supervised fine-tuning settings. (3) INSTRUCTRAG consistently outperforms state-of-the art RAG approaches, yielding relative improvement of 8.3% on average compared to the best baseline method across five mnowledge-intenssee benchmarks. Ex- tensive analysis and ablation studies further confirm the superiority of self-synthesized denoising rationales, and demonstrate INSTRUCTRAG's robust denoising ability against increased noise ra- tios and strong task transferability in various training- free and trainable scenarios.
2 OUR METHOD: INSTRUCTRAG
In this section, we first introduce our problem setting ($ 2.1) and then present the proposed frame- work INSTRUCTRAG that enables LMs to explicitly denoise retrieved contents. As shown in Fig- ure 2, our method consists of two steps. First, we prompt an instruction-tuned LM (i.e., rationale generator M2) to synthesize rationales that provide denoising supervisions ($ 2.2). These rationales aim to explain how to derive the correct answer from potentially noisy retrieved documents for each training sample. Then, we guide the LM (i.e., rationale learner MMp) to learn explicit denoising by leveraging these rationales either in-context learning demonstrations or supervised fine-tuning
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Step 1: Rationale Generation for RAG
Step 2: Explicit Denoising Learning
Denoising Instruction
Retrieved documents
Learning
Supervised Learning
Doc
Language Model My
Language Model M, (Trainable)
Question
Language Model 2 (Frozen)
(Frozen)
Doc di
Prompt: cinstructionn a5 demonstrations
Prompt: cinstructions as training data
Dac dic
Rationale
second document mentions... None of the other documents relevant...
Question!
Retionale
Doc d
Doc
Therefore,
canswers
Figure 2: An overview of INSTRUCTRAG. In step one, given the question g, retrieved documents {d1.... dx) } and ground-truth answer from the training set, we prompt instruction-cunee LM (i.e.. rationale generator Ma) to generate rationale that explains how the answer can be derived from the potentially noisy input. In step two, utilize the synthesized rationales from the first step to guide the LM (i.e., rationale learner M2) to explicitly learn denoising of the retrieved documents, either through in co-context learning or supervised learning. By default, use the same model for both Ma and Ma, but they can be instantiated with different models as well (see ablation study $ 3.3).
Table 1: Rationale generation prompt for the i-th training sample.
Rationale Generation
Input: Read the following documents relevant the given question: {9:}
Document [1] (Title: :. {contents of (
Please identify documents that useful to answer the given question: { and explain how the contents lead to the answer: {a,}
[task-specific instruction]
Output: (rationale r
data ($ 2.3). As detailed in Algorithm 1, during the entire process, INSTRUCTRAG does not require any additional supervisions beyond standard RAG methods. By default, we instantiate both Ma and MM with the same off-the-shelf instruction-tuned model (i.e., metaa-lama/Mett-Lllaan -latt---lla----8--- Instruct), making INSTRUCTRAG a fully self-syythesis method. We also experiment with different instantiations of Ms and Ma and conduct ablation study in both training-free and trainable set- tings ($ 3.3). For simplicity, we use placeholders to represent omitted instructions in the prompts presented in this section, while the full list of complete prompt templates is provided in Appendix D.
2.1 PROBLEM SETTING
We adopt the standard RAG setting where the LM Ma has access to annotated datasets of down- stream tasks (e.g., question-answering task = (gg, a) }), and an external knowledge base with the off-the-shelf retriever R for retrieval. Different from previous works (Asai et al., 2023b; Yoran et al., 2024) which leverage additional supervisions from GPT-3 (Brown al., 2020) or GPT-4 (Achiam et al., 2023), we assume the model has strictly limited access to the above two information sources. Given a question q, the retriever R returns set of potentially noisy documents from the external knowledge base. The model is then tasked predict the correct answer to the given question q based on 1 and its own parametric knowledge, denoted as ps (alq. D))
(di,... dxx)
Our work focuses on investigating the noise robustness of LMs and developing efficient denoising techniques for RAG. Hence, we directly employ off-the- the-shelf retrievers instead of training our own, and prepend all retrieved documents to the question input to the model, without any filtering or re-ranking. This setting is orthogonal to existing research efforts centered on optimizing the retriever performing adaptive retrieval (Asai et al., 2023b; Wang et al., 2024a; Yang et al., 2024).
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Algorithm INSTRUCTRAG
Require: Retriever R. Rationale generator Ma. Rationale learner Ma. Training data T = {(g. a))
/* Training data generation */
1: for each (q,a) T do
2: Retrieve D = (d,,..., ,.k....,d,]] + R(g)
3: Synthesize denoising rationale +- M Mo(g,a,))
> Rationale Generation ($ 2.2)
4: Augment training data T T+ [(g,r)}
* Two learning modes +/
5: if LearningMode = In-Context Learning then
b. INSTRUCTRAG-ICL
6: Sample ICL examples & ((g,r))
7: T T Maall,, R(q), given inference query !
b Detailed in Table 10 INSTRUCTRAG-FT
8: else if LearningMode == -aninnnnn then
9: Fine-tune Ma on T+ with retrieved documents ((g.r, D)}
r Mo R())) given inference query q
Detailed in Table 11
11: return
2.2 RATIONALE GENERATION VIA FOSTRIC
Recent studies (Leike et al., 2018; Meng et al., 2024; Ouyang et al., 2022) have made encouraging progress in aligning LMs with human preferences and intentions, enabling the synthesis of high- quality data that closely follows user instructions (Xu et al., 2024c). Inspired by these advances, we propose leverage the LM's strong Instruction-followwe responses (i.e., rationales) for RAG. As shown in Table I, given QA pair (9. a,) T and a set of retrieved documents {d) ... a²k }. we prompt an off-the-shelf L.M Mo (as the rationale generator) with denoising instructions to produce the corresponding rationale r; that distinguishes useful docu- ments from noisy ones and explains how the contexts lead to the ground -ruth answer a, To ensure the synthetic rationales are aligned with the ground-truth answers, use a simple substring match to assess their consistency. The consistency ratio on training samples with at least one relevant doc- ument containing the ground-truth truth answer is 98% average across five benchmarks, supporting the reliability of synthetic rationales as sanity check. This allows us to effectively augment the standard dataset T = a4 T (g.r)) with self-synthesized denoising rationales solely by instructing the LM, without any additional supervision.
ability to generate explicit denoising
We also validate the necessity of using an LM-based generator (i.e., MMa) to create the rationales instead of employing simple heuristics - without the generator, rationales can be created in template-based manner (Table 6), by roughly identifying relevant retrieved documents through sim- ple substring matching with the ground-trudh answer. However, as demonstrated in our ablation study, this approach suffers from semantically inaccurate matching of relevant documents, leading to significant performance degradation. Another advantage of the LM-based generator is that it can produce high-quality rationales even without referring to the ground-iruth unswer, which only re- sults in minor performance drop. More detailed analyses rationale generation design can be found in our ablation study (S 3.3).
2.3 LEARNING DENOISING RATIONALES IN RAG
With the aetionale-augmennn dataset T+ it becomes possible to develop a rationale learner Ma that directly learns explicit denoising for RAG tasks with efficient learning strategies. Next, we introduce two simple yet effective learning methods in the training-free and trainable RAG settings, namely, INSTRUCTRAG-ICL and INSTRUCTRAG-FT.
INSTRUCTRAG-ICL is a training- free instantiation INSTRUCTRAG where the model learns denoising rationales via in-context learning (ICL), As shown in Table 10, given test question and a set of retrieved documents D = {di..... dx }. we first randomly sample N demonstrations (4,, T from the rationale- augnentee training dataset, and then prompt the model to follow the exemplars and generate rationale r. To save memory and enhance inference efficiency, we only show exemplary questions and their corresponding rationales in such ICI demonstrations.
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INSTRUCTRAG-FT. is trainable instantiation of INSTRUCTRAG that learns denoising rationales via supervised fine-tuning (FT) with standard language modeling objective. As defined in Eq. 1, maximizes the likelihood of rationale r conditioned question q and retrieved documents D.
max R. (ggr))+++ log po D).
where l represents the model parameters. Both the training and inference of INSTRUCTRAG-FT share the same data format. As depicted in Table 11, it takes as input the retrieved documents followed by the question, and outputs the denoising rationale r.
EXPERIMENTS
3.1
EXPERIMENTAL SETTING
Table Dataset statistics and retrieval setting.
Dataset
Train
Test
Retriever
Top-K
Recall @ K
PopQA
12,868
1,399
Contriever
5
68.7
TriviaQA
78,785
11,313
Contriever
73.5
Natural Questions
79,168
3,610
DPR
5
68.8
ASQA
4,353
948
GTR
5
82.2
2WikiMultiHoPQA
167,454
12,576
BM25
10
40.7
RAG tasks and evaluation metrics. We extensively validate the effectiveness of INSTRUCTRAG on five -edgeeintensive benchmarks, including PopQA (Mallen et al., 2023), TriviaQA (Joshi et al., 2017), Natural Questions (Kwiatkowski et al., 2019), ASQA (Stelmakh et al., 2022), and 2WikiMultiHopQA (Ho al., 2020). We use Wikipedia corpus the retrieval source, and test method with both sparse and dense off-the-shelf retrievers, including BM25 (Robertson & Walker, 1994), DPR (Karpukhin al., 2020), GTR (Ni et al., 2022) and Contriver (Izacard al., 2021). The retrieval quality is measured by Recall @ K, indicating whether the retrieved K documents contain the correct answer. Table shows the detailed dataset statistics. Following standard evaluation set- tings (Asai et al., 2023b), we adopt the official metric of correctness (str-em), citation precision (pre) and recall (rec) for ASQA (Gao et al., 2023a), and use accuracy for the other tasks, which measures whether the ground-truth answers are included in the model generations (Mallen et al., 2023; Schick al., 2023). Additionally, we also adopt I.LM-as-a-jude for further evaluation ($ 3.4), as the above standard metrics subject to the limitations of waatern-matching which cannot accurately handle semantic equivalence.
Baselines. We compare our method with wide range of RAG baselines under both training- frree and trainable settings. Given that state-of-thh-aan LMs have incorporated a large amount of world- knowledge during the pre-training stage, also report the performance of a non-retrieval baseline (namely, vanilla zero-shot prompting) for reference. Specifically, the training-free RAG baselines includes: (1) in-context retrieval-augmented language modeling (RALM) (Ram al., 2023), prompting method that extends the non-retrieval baseline by presenting the model with retrieved doc- uments: (2) few-shot demonstration with instruction, ICL method using ground question- answer pairs sampled from the training set as demonstration exemplars.
The trainable RAG baselines include: (1) vanilla supervised fine-tuning (SFT), supervised method with the training objective of maximizing the data likelihood of ground- truth answer given potentially noisy input; (2) RetRobust (Yoran al., 2024). which fine-tunes the RAG model on mixture of relevant and irrelevant contexts to make it robust to irrelevant contexts; (3) Self- RAG (Asai al., 2023b), a strong trainable baseline, focusing on adaptive retrieval controlled by special reflection tokens. Both RetRobust and Self-RAG were originally built Llama-2 (Touvron et al., 2023) with additional supervisions. For example, RetRobust augments the training data for multi-hop reasoning tasks (e.g., 2WikiMultiHopQU. by prompting GPT-3 to decompose the origi- nal query and generate intermediate subqueries, and Self-RAG requires GPT-4 to generate additional reflective tokens augment training samples.
For a fair comparison, we re- re-implement the two methods on Llama-27B and/or Llama-2 with augmented training data released by their authors, and report their performance as the higher one
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Table Overall results of INSTRUCTRAG and baselines on five knowledge-intensive benchmarks in training-free free and trainable RAG settings. We re-implement baselines and report their performance as the higher one between the original scores and our reproduced results. indicates the results copied from Asai et al. (2023b) for reference. "_" indicates the results are not reported in the original paper or not applicable (e.g., some methods cannot produce citations). The best performance highlighted in bold.
PopQA (acc)
TriviaQA (acc)
NQ
MultiHopQA (acc)
ASQA (pcc)
Method
(acc)
(cm)
(rec)
Baselines w/o Retrieval
Vanilla Zero-shot Prompting
ChatGPT
29.3
74.3
35.3
Allmm--3-Insteeee
22.8
69.4
46.6
45.6
30.6
-lama-3-Instructiue
28.9
80.6
57.9
57.5
39.1
RAG w/o Training
In-Context RALM (Ram al., 2023)
ChatGPT
65.7
40.7
65.1
76.6
62.3
71.4
56.8
43.4
40.0
62.1
66.4
-lama-3-Insstrucee
63.8
76.3
60.2
51.2
43.1
62.9
67.6
Few-Shot Demo. w/ Instruction
74.2
45.3
42.6
55.0
Llama-3- I-nstruccnn
63.9
79.1
62,9
53.9
45.4
49.3
57.1
ISTRUCTRAG-ICE 1amna-3-Instrutts
64.2 65.5
76.8
62.1
50.4
44.7
70.9
74.1
81.2
66.5
57.3
47.8
69.1
71.2
RAG w/ Training
Vanilla Supervised Fine-tuning
-lama-3-Instrucht
61.0
73.9
56.6
56.1
43.8
Self-RAG (Asai et al., 2023b)
Laama-27a
55.8
68.9
42.4
35.9
30.0
66.9
67.8
Llama-213n
56.3
70.4
46.4
36.0
31.4
70.3
71.3
Llamma--Instruotsn
55.8
71.4
42.8
32.9
36.9
69.7
69.7
RetRobust (Yoran et al., 2024)
Llama-21a
39.6
51.5
Llama-3- Instrruct,
56.5
71.5
54.2
54.7
40.5
INSTRUCTRAG-FT Llama-3-Instrucck
78.5
65.7
57.2
47.6
65.7
70.5
between the original scores and our reproduced results. As our method adopts instruction-tun Llama-3 the backbone model, also train RetRobust and Self-RAG with Llama-3-Instructss and optimize their performance through extensive hyper-parameters search. More details on im- plementation, including training, inference, and prompt design are available in Appendix and Appendix D. We also present some case studies in Appendix C.
3.2 MAIN RESULT
Table shows the overall experimental results, providing a comprehensive comparison between our INSTRUCTRAG and baseline methods in both training-free and trainable RAG settings.
Baselines without retrieval. As shown in the first block, the basic instruction-tunel models (Llama- Instructy and 3amma-3-Instuutrru already achieve notable performance across all five bench- marks, with the 70B model exhibiting surprisingly competitive performance of 80.6% on the Triv- iaQA. This observation suggests that the required knowledge for these tasks mostly falls within the LM's parametric knowledge, probably due to what is known data contamination (i.c., the pres- ence test data of downstream tasks in the pre- pre-training data LMs) (Golchin & Surdeamu, 2023; Jacovi et al., 2023; Magar & Schwartz, 2022).
RAG without training. The second block shows the comparison among training-fres RAG meth- ods. In-context RALM and few-shot demonstration with instruction methods generally achieve higher performance than the non-retrieval baseline, highlighting the importance of retrieval for knowledge-intensive tasks. Encouragingly, INSTRUCTRAG-ICI consistently outperforms all

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Providing ground-truth answers and retrieved documents is important for rationale genera- tion. As depicted in the first block of Table 4, we ablate the rationale generation design from two aspects: (1) w/o ground-truth answer, where the model has no access to the ground-truth answer during rational generation and must predict the answer and explain how it is derived solely based on retrieved documents; (2) w/o retrieved documents, where the model is not provided with any retrieved documents during rational generation, and in this case, it has to explain the given answer based on its own knowledge. Although it is not surprising that our default design consistently out- performs the two ablations, it is encouraging to find that our method still works well even without access to the retrieved documents or ground-truth answers. This finding suggests the great potential fully unsupervised manner, which we believe is an exciting Published as a conference paper at ICLR 2025
Table 4: Ablation study on the impact of ground-truth answer, retrieved documents, and model size on rationale generation, and the use of demonstrations during model inference. The results of our default setting in INSTRUCTRAG are underlined.
Trainable RAG Setting
Training-free RAG Setting PopQA
Method
PopQA
ASQA
ASQA
Rationale Generation Design
with both
66.2
47.6
64.2
44.7
w/o ground-truth answer
65.2 (1 1.5%)
46.4 (L 2.5%)
64.0 (L 0.3%)
44.5 (4 0.4%)
w/o retrieved documents
64.5 (L 2.6%)
45.2 (1 5.0%)
64.1 (L 0.2%)
44.3 (4 0.9%)
Model Size of Rationale Generator
rationale template (no generator)
59.6 (4 10.0%)
46.3 (L 2.7%)
60.0 (4 6.5%)
41.4 (L 7.4%)
Llama-3-Instruct (8B) Llama-3-Instruct (70B)
66.2
47.6
64.2
44.7
67.0 (1 1.2%)
49.1 (1 3.2%)
64.8 (1 0.9%)
47.9 (1 7.1%)
Inference Strategy Comparison
w/o demonstration
66.2
47.6
63.0 (4 1.9%)
43.1
(L 3.6%)
w/ demonstration
66.1 (4 0.2%)
44.7 (1 6.1%)
64.2
44.7
training-free baselines across various metrics, confirming the effectiveness of self-synthesized de- noising rationales. Moreover, the boost from 8B 70B model indicates that INSTRUCTR AG-ICI scales effectively with larger backhone models, validating the generalizability of our method.
RAG with training. As present in the bottom block Table 3, our INSTRUCTRAGFFT not only sur- passes all non-retrieval and training-frre baselines across all five benchmarks, but also significantly outperforms trainable RAG baselines on almost every metric. The only exception is in the ASQA task, where our method slightly underperforms Self-RAG in terms of citation (i.e., pre and rec). This is because our work primarily focuses on explicit denoising for RAG to improve the correct- ness of generations, which is measured by em. Despite not being explicitly optimized for citation metries, our method still achieves competitive citation performance, significantly enhancing both generation accuracy and trustworthiness. Note that RetRobust achieves competitive performance on 2WiiiMultHHooAA, which involves multi-hop reasoning. We attribute this to the additional training supervision provided by GPT-3, which enables the model to explicitly generate intermediate sub- queries and sub-answers. Another interesting finding is that Self-RAG consistently exhibits inferior performance compared to vanilla SFT, and even underperforms the training free in-context RALM baseline across all benchmarks. We speculate the reason might be that these RAG tasks favor more domain-specific knowledge than general knowledge. However, it is challenging for Self-RAG to directly leverage in-domain features from existing training data as it requires GPT-4 to generate reflection tokens on these benchmarks, which is not available in our problem setting ($ 2.1).
3.3 ABLATION STUDY
of our INSTRUCTRAG AG to operate direction for future work.
Larger rationale generator leads to better results. The middle block shows how different sizes of rationale generators impact the performance of our method. It is evident that the template-based rationale generation method significantly underperforms our method, highlighting the necessity of rationale generator. This is because the template-based based method relies on pattern matching to identify relevant documents containing the ground truth answer, which only considers lexical sim- ilarity while ignoring semantic meaning. The neglect of semanties inevitably introduces noise in
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Roodat Dana of lodasii
Accuncy (PoytAA)
20
Presdct Dras my hetmette
90
Accuncy (Pop(2A)
70
Veuilla SPT
EM (ASQA)
46
Pro- Prn-biimnii
60
03
8
60
42
3
7
9
of Gemoustcations
of intrissod dooemmnnts
retaloued documents
(a) Training-fre free RAG setting.
(b) Training- RAG setting.
(c) Trainable RAG setting.
Figure 3: Impact of different number of demonstrations and retrieved documents. (a) Demonstration sensitivity study of INSTRUCTRAG-ICL. (b) Noise robustness study of INSTRUCTRAG-ICL. (c) Noise robustness study of INSTRUCTRAG-FT:
template-generated rationales, making them less effective compared to rationales generated by LMs. Moreover, we also compare two variants of INSTRUCTR AG using Instruct708 as rationale generators. The results show that the one with a 70B generator consistently outperforms its 8B counterpart in both training- free and trainable settings, indicating that the self- synthesized denoising rationales can provide better supervision when generated by stronger models.
and Llama-3-
Inference with demonstrations should only be applied to INSTRUCTRAG-ICA. In the bottom block, we study the use of demonstrations during the model inference. While demonstrations play an important role for INSTRUCTRAG-ICL. we find that they actually hurt the performance of IN- TRUUTRAG-FF. We attribute this to the fact that INSTRUCTRAG-Fi is optimized to directly gen- erate denoising rationales given potentially noisy input, without referring to any demonstrations. Therefore, providing in-context demonstrations for INSTRUCTR AG-FT is redundant and may com- promise its capability due the discrepancy between training and inference.
3.4 ANALYSIS
INSTRUCTRAG-ICL consistently benefits from more demonstrations. Figure 3a shows the demonstration sensitivity of INSTRUCTRAG-ICI and the few-shot demonstration with instruction baseline. It is interesting to find that the baseline method achieves its best performance with only one demonstration, and presenting more demonstrations actually harms its performance. In con- trast, our method consistently improves with the increasing number of demonstrations, confirming the superiority of self-synthesized rationales over plain answers in terms of denoising.
INSTRUCTRAG-ICL. and INSTRUCTRAG-FT are robust to increased noise ratios. Figure 3b and Figure 3c show the generation accuracy of INSTRUCTRAG-ICL and INSTRUCTRAG-FT and the corresponding retrieval precision under increasing number of retrieved documents. While retrieving more documents provides richer external knowledge to the RAG model, it also intro- duces more noise and lowers the retrieval precision. As result, both the training- free and trainable baselines show diminishing improvements or even degrade as the number of documents increases, reflecting their vulnerability to high noisy ratios. In contrast, our INSTRUCTR, AG-ICI and IN- STRUCTR AG-FT are not negatively affected by this increased noise ratio but rather gain further improvement, demonstrating their robust denoising ability.
INSTRUCTRAG-ICL and INSTRCCTRAGE generalize well to unseen tasks. Figure 4 demon- strates the generalization ability of our method in both training-free and trainable settings. For the in-domain (ID) method, it directly utilizes target domain demonstrations (in training-free settings) or is trained on the target domain task (in trainable settings). In contrast, the out-of-domain (OOD) method can only learn from demonstrations or training data in the source domain, and have no prior knowledge of the target domain. In this case, the model must leverage the knowledge learned from the source domain task to solve the unseen target domain task. The results show that our method consistently outperforms the baselines across various scenarios in both in-domain and out- of-domain settings, demonstrating strong task generalizability. One counter-intuitive finding is that in the scenario of generalizing from long-form to short-form QA task (Figure 4b), the training -feee OOD method substantially outperforms its in-domain counterpart. We speculate that the training- free OOD method achieves better performance because it benefits from the demonstrations with long answers from the source domain (ASQA). The reason is that the questions in ASQA ambiguous
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50+
Accunary (Pxp1A)
Accuracy (Multillog))
66
EM (ASQA)
40
-0-
20-
20
Triinin Fiee
Thinable
Trining Free
Training
Taiiable
(a) Short-form to long-form QA.
(b) Long-form to short-form QA.
(c) Single-hop to multi-hop QA.
Figure 4: Generalizing INSTRUCTRAG from source domain task to target domain task, where ID and OOD denote in-domain and out-of-domain settings, (a) PopQA (short-form QA task) as source domain and ASQA (long-form QA task) target domain. (b) ASQA source domain and PopQA as target domain. (c) PopQA (single-hop QA task) as souree domain and 2WikiMultHHopAA (multi- hop QA task) as target domain. We adopt shot demonstration with instruction and vanilla su- pervised fine-tuning as the training-free and trainable baselines.
Table 5: (a) Transfer from the source QA task (PopQA) to the target code generation task (Hu- manEval). Our method INSTRUCTRAG-FT is fine-tuned only on the source task and is evaluated on the unseen target task. We compare it with off-the-shelf LLaMA-3-8B-Instuu using the stan- dard metrics pass@k (Chen et al., 2021), in both non-retrieval and eetrieval-augmens settings. (b) Evaluation with GPT-4o the judge. Compared to pattern-matching based metries, allows the judge to consider semantic equivalence and is expected to yield more fair evaluation.
generation
Method
pass@1
pass @ 10
Method
Pattern-based LLM-based
Without Retrieval
RAG w/o Training
Llama- 1imma-3-BB-Inttruus
58.5
64.6
In-Context RALM
56.8
64.5
INSTRUCTRAG-FT
60.4
65.2
INSTRUCTRAG-IC
62.1
67.6
With Retrieval
RAG w/ Training
Llama---BB-nssrue 59.8
69.5
Vanilla SET INSTRUCTRAG-FT 65.7
56.6
65.1
INSTRUCTRAG-FT 64.6
71.3
69.7
(a) Transfer from QA task to code generation task.
(b) Evaluation with GPT-4o the judge.
and can have multiple interpretations, and ground- truth long answers often address the questions from various perspectives, which can be regarded as a form of chain-of- demonstration.
Furthermore, we also study the generalizability of INSTRUCTRAG to a non-QA knowledge- intensive task such code generation. As presented in Table 5a, we directly apply INSTRUCTRAGE FT trained on the QA task (PopQA) to solve the unseen code generation task (HumanEval (Chen et al., 2021)), following the CodeRAG-Bench setup (Wang et al., 2024c). We evaluate the code gen- eration performance using the standard pass@k k metric and compare our method with the off-the- shelf Llama-3-88 3-8B-Instruct the baseline. It can be observed that our method consistently achieves better generalization performance in the unseen code generation task in both non-retrieval and RAG settings. This finding aligns with observation that INSTRUCTR AG trained QA tasks tends to generate more text-based comments that articulate the design of coding solutions compared to the off-the-shelf 1amaa---B-IIntruuue thereby leading more accurate code generation.
Evaluation with LLM-as---juddee Despite being standard evaluation metrics for question- answering, accuracy exact match known be imperfect (Cuconasu al., 2024) they mainly rely on pattern-matching to judge the correctness of model predictions. Such metrics cannot handle cases where the prediction and ground-truth are synonyms (c.g., "Donald Trump" vs "Donald J. Trump" cannot be correctly recognized as a match), leading to biased evaluation results. Therefore, we use juddee (Bubeck al., 2023; Zheng et al., 2024b) to evaluate the predictions with GPT-4o (OpenAI, 2024), which allows the judge to consider semantic equivalence and is expected to yield a more fair evaluation. As shown in Table 5b, we evaluate our method and baseline models on the open-domain Natural Questions benchmark in both training-free and trainable RAG settings. Compared to results, mostly due to its capability to accurately match semantically equivalent phrasings. Notably, our method consistently outperforms baselines under both pattern-matche matching based and LLM-based evaluation metries, further validating the effectiveness of INSTRUCTRAG:
based metrics, LLM-as ALLM-s-a-jjude j-jdge generally leads to higher evaluation

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4 RELATED WORK
4.1 RETRIEVAL-AUGMENTER GENERATION
Retrieval-augmente generation (RAG) is a widely adopted approach to enhance large language models (LLMs) with external knowledge (Asai et al., 2023a; 2024; Borgeaud et al., 2022: Gao et al., 2023b; Guu et al., 2020; Khandelwal et al., 2019; Lewis et al., 2020; Ram et al., 2023; Shi et al., 2023), demonstrating promising potential to reduce hallucinations and enhance the generation accuracy of LLMs across various real-world applications (Chase, 2022; Jin et al., 2024b; Liu, 2022; Lu et al., 2022: Siriwardhana al., 2023; Tan et al., 2024; Zhou al., 2022; Liu al., 2024b; Xiong et al., 2025). Recently, growing research effort has been devoted to enhancing RAG from various aspects, such as improving decoding efficiency (Merth al., 2024; Jin et al., 2024a; Liu et al., 2023; Wang et al., 2024b), exploring long-context retrieval (Xu et al., 2024a; Yen et al., 2024), compressing prompts (Jiang et al., 2023a; Xu et al., 2023; Cheng et al., 2024), and addressing practical concerns such as adversarial retrieval (Xiang et al., 2024; Zhong al., 2023; Zou et al., 2024) and privacy leakage (Huang et al., 2023b; Zeng et al., 2024). Despite their advantages, these RAG systems inevitably suffer from irrelevant information introduced by imperfect retrievers or noisy retrieval corpora. However, most existing works typically address this issue by improving the retrieval quality and reducing noise exposure to the model (Gupta et al., 2024; Jiang et al., 2024a; Sarthi et al., 2024; Wang et al., 2024a; Yan et al., 2024; Yang et al., 2024; Zhang et al., 2024a;b). Notable methods include adaptive retrieval (Asai al., 2023b; Jiang al., 2023b; Yao et al., 2022) and query rewriting (Chan et al., 2024; Mao et al., 2024). In contrast, our work focuses on an orthogonal direction of developing explicit denoising methods for RAG, thereby enhancing the model's noise robustness and generation accuracy, even in highly noisy contexts.
4.2 ELICITING REASONING IN LARGE LANGUAGE MODELS
Recent studies have extensively explored the reasoning capability of LMs, but typically not in the context of RAG where potentially noisy retrieved contents may mislead the reasoning if not properly addressed. Chain-of of of-thhught (CoT) prompting (Wei et al., 2022) is an effective method to elicit step- by-step reasoning from LMs by showing exemplars with detailed explanations (i.e., rationales (Feng et al., 2024; Lampinen et al., 2022; Rajani et al., 2019; Zelikman et al., 2024)) that lead to the final answer. However, such works often requires manually crafted demonstrations (Wang et al., 2022; Xu et al., 2024b), which is costly and requires extensive efforts and domain knowledge (Zheng et al., 2024a). To mitigate this limitation, other methods have been introduced to automatically select instances from the corpus (Zhang al., 2022) or curate demonstrations by the LM itself (Chen et al., 2023a), coupled with zero-shot CoT (Kojima et al., 2022) to generate rationales. Furthermore, it has been shown that CoT reasoning can be elicited even without explicit prompting, particularly for instruction-tunee LMs (Wang & Zhou, 2024). Another related work shows rationales generated by small models can help large models reason better (Lee et al., 2024). Although rationalization has been extensively investigated in many NLP tasks (Chen et al., 2023b; 2022; Ghoshal et al., 2022; Paranjape et al., 2020; Wiegreffe al., 2021), none of them are designed for RAG, and how to leverage the instruction-follont following abilities of LMs for explicit denoising in the context of RAG still remains underexplored.
5 CONCLUSION
In this work, we presented INSTRUCTRAGE AG, a simple retrieval-augmenned generation (RAG) ap- proach that explicitly denoises retrieved contents and produces accurate generations. By leverag- ing the strong instruction-following abilities of large language models, INSTRUCTRAG generates detailed rationales that articulate how the ground-truth answers can be derived from the retrieved documents. These synthetic rationales can serve as either in-context learning examples or super- vised fine-tuning data, enabling the model to learn an explicit denoising process. Experiments on five Knowledge-intensive benchmarks show INSTRUCTRAG consistently outperforms state-of- the-art RAG approaches with significant improvements in both training-fre free and trainable settings. Compared to the best baseline method, INSTRUCTRAG achieves an average improvement of 8.3% across all benchmarks, demonstrating its effectiveness in enhancing the noise robustness of retrieval- augmented generation. Limitations and future work are discussed in Appendix A.
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ACKNOWLEDGMENTS
The authors would like to thank Xinyu Zhu from University of Virginia, Tianyu Gao and Zexuan Zhong from Princeton NLP group for their valuable feedback and discussions. This research was supported in part by the NVIDIA Academic Grant and the OpenAl Researcher Access Program. We thank anonymous reviewers for their constructive and insightful comments.
REPRODUCIBILIITY STATEMENT
To ensure the highest level of reproducibility for our reported results, we have provided:
Complete source code, accessible via the following link: httpp://gitiiei com/ weizhepei/Instructee Comprehensive implementation details in Appendix B;
All prompt templates used in our experiments in Appendix D.
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A LIMITATIONS AND FUTURE WORKS
Limitations. In this work, we mainly conduct experiments on question answering-type tasks, and remains unclear how our method may generalize to other scenarios (e.g. open-ended generation). Moreover, despite being the standard evaluation metrics, both accuracy and exact match are biased and cannot perfectly reflect the quality of the model's generations. For instance, such metrics heav- ily rely on string matching, which assesses correctness at the lexical level rather than the semantic level, thereby failing to recognize different phrasings that convey identical meanings. The evalu- ation results also suffer from length bias, as longer generations tend to achieve higher accuracy. Exploring more advanced metrics like using LLMs as judges would better evaluate RAG model generations (Yang et al.. 2024). Another potential limitation that our model might be subject to sample bias in the training data. Incorporating bias mitigation methods (Gallegos et al., 2024; Kawamae, 2023; Yang et al., 2023) would be helpful for further improving our work.
Future Work. Future research directions include exploring more advanced techniques for gener- ating high-quality rationales, such incorporating domain-specifie knowledge or leveraging multi- task learning to enable better generalization across various tasks. For instance, although the consis- tency ratio between synthetic rationales and ground-truth answers on training samples with least one relevant document achieves 98%, the overall consistency ratio on all training samples is only 89%. This is because for some samples, none of the retrieved documents relevant to the ques- tion, which significantly compromises the quality of the generated rationales. Therefore, it will be interesting to fully explore the potential of our method by incorporating additional designs such as a filtering mechanism, which we leave as future work. It will also be interesting to evaluate the model performance under long-context settings with a dynamic or extremely large number of retrieved doc- uments. Finally, integrating our method with other advanced retrieval techniques (Su et al., 2024), such as active retrieval, could potentially lead to even better performance on knowledge-intensiie tasks.
B IMPLEMENTATION DETAILS
Retrieval setup. Following (Asai et al., 2023b; Ram et al., 2023), we use the Wikipedia dump from (Karpukhin et al., 2020) as the external retrieval corpus for all five benchmarks studied in this work, where each document a disjoint text block of up to 100 words extracted from a Wikipedia article. We compared all RAG methods under diverse retrieval environment with various sparse and dense retrievers and number of retrieved documents. Specifically, we use Contriever-Me MARCO as the retriever for PopQA and TriviaQA, DPR for Natural Questions, GTR for ASQA, and BM25 for 2WikiMultiHopAA By default, we retrieve the top documents from the retrieval corpus for each query in all tasks except 2 2WikiMultiHopQA, where the top 10 documents are retrieved. We use the official weights for all dense retrievers and the implementation from Pyserini (Lin et al., 2021) for the sparse retriever BM25.
Training details Our models are trained on 4 Nvidia H100 GPUs with 80GB memory via full- parameter fine-tuning. We use fully sharded data parallelism (FSDP) for distributed training, along with FlashAttention (Dao, 2023) and b mixed precision training enabled for computation effi ciency. By default, all models are trained using the Adam optimizer (Kingma & Ba, 2014) for epochs, with batch size of 128, a learning rate of 2.5e-5, and a cosine learning rate schedule with 3% warmup steps. For the trainable baseline vanilla SFT, we use a slightly different learning rate of 2e-5 based on our hyper-parameter search results. To fairly compare with Relf-RAG and RetRobust, we re-implement them using Llama-3. I-nst--uttttttt8 We also optimize their performance through an extensive syper-paramete search with learning rates in [8e-6, le-5, 2e-5] and training epochs in [1, 2, 3]. For Self-RAG, we use learning rate of le-5 with a single training epoch. For RetRobust, we use learning rate of 2c-5 with two training epochs. The only exception is the training for RetRo- bust on 2WikiMultHHoQA. where we train the model for 5 epochs on the augmented training set released by the original authors. The maximum token length for all models is fixed at 4096.
Inference details. By default, the number of demonstrations used in INSTRUCTRAG-CC and the baseline method few-shot demonstration with instruction is set to be 2. We use vLLM (Kwon al., 2023) to load models for memory-efficien inference and adopt the greedy decoding strategy for model generation.
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C
CASE STUDY
Attention from Answer Documents
Attention from Answer to Documents
Doe 1-
Doc 1
0.6
0.6
Doc 2
Doc 3-
Doc 3-
Doe
Doc 4
Doe 5-
0.2
0.2
22
23
29 Last. 10 Atlention Layers
28
30
28 Last 10 Attention Layers
27
29
30
Vanilla SFT.
INSTRUCTRAG-F
Figure 5: Visualization of model attention from answer to retrieved documents on a random sample from the ASQA task, where Doc 2 is the only relevant document that contains the correct answer.
Question: Who played the admiral Star Trek: Discevery? Katrina Cornwell, portrayed by
Starfleet admiral.
Contexts: Document 1 (Title: Star Trek: Insureectinn.... Document (Titl: Star Trek: Discovery (seeson tjl.... Document 3 (Title: Star Trekf.... Document (Title: Bran Thompsonj:.... Document 5 (Title: Star Treic: Enterpriaey....
According to Document 2, there no mention of an Admiral character in Star Trek: Discovery, However, according Document 3, Captain Gabriel Lorca (played Jason Isaass) commander the USS
X Wrong Answer
Vanille SFT
Star Trek: Discovery an American television series created by Bryan Fuller and Alex Kurtzman for the streaming service CBS Access. The series is set about decade before the events of the original Star Trek series and follows the crew of the USS Diecovery they attempt massive Klingon ammada while
the truth about the Red Angel and Section 31. The character of Admiral
Corwwell played by Jayne Enterprise NCC 1701 D. The character Admiral Convell is played by Elizabeth Maxwall.
The character
Comwell
serves
USS
X Confusing Anawer
InstructRAG-FT
that
played admiral
Discovery?"
Trak
Insurrection, Document 3: Star Trek, Document Star Trek Enterprise. These documents provide information about the casting actors in Star Trek productions, which relevant to the question From Document 1, we learn that Anthony Zarbe played the role of Admiral Matthew Dougherty Star Trek: Insurection. From Decument Star Trek: Discovery
Star Trek franchise Enterprise. However, none these documents mentian the specific admiral on Star Trek Discavery, which is Admiral Cornwlll, played by Jayne Brook. The correct answer is not found in the provided documents, but it can be arsivered based external knowlddee. Jayne Brook played the rale of Admiaal Trek Discovery.
premiered in 2017.
From Dosument
learn Vaughn Armstonng played Admiral Maxwell Forest
Correct Answer
Figure 6: A case study of INSTRUCTRAG-FT compared with in-context RALM and vanilla SFT. The red texts denote irrelevant inaccurate model generations, while the green texts denote tents relevant to the question. This study shows that our model can effectively identify relevant information from noisy input and leverage its knowledge correctly answer questions when required.
Attention visualization. To intuitively understand the denoising process of our INSTRUCTRAG, we visualize its attention from the answer to retrieved documents. As pointed out by a recent work (Yu al., 2023), only attention distributions from deep layers can accurately reflect the LM's retrieval behavior and focus on key information, while attention from shallow layers usually do not imply meaningful patterns. Therefore, we only plot the attention weights of the last 10 layers (Layer 22 to Layer 31). As presented in Figure 5, our model accurately identifies the only benign document from noisy input, showing a strong denoising signal compared to vanilla SFT.
Generation comparison. Figure 6 compares the generated responses of in-context RALM, vanilla SFT, and our INSTRUCTRAG-FT for an actual question from the ASQA task. Among them, only method can correctly answer this question while providing comprehensive denoising details. Specifically, it first identifies potentially relevant documents from noisy inputs, and then lays out the candidate information. More encouragingly, its own parametric knowledge when no relevant document is present in the context after denoising, demonstrating its superiority over existing RAG approaches.
find that INSTRUCTRAG-PF is able to refer to
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D PROMPT TEMPLATES
In this work, we instantiate the proposed INSTRUCTRAG with off-the-shelf instruction-tuned LMs (i.e., La-lam///aa---aaa------------ ply the official Meta-Llama--3Instruee chat template (marked in gray) in all prompts.
and (atmaa//Maa-Llma---00000
and ap-
Rationale generation. Below the prompt templates for rationale generation used for all five benchmarks in our work. Table 6 shows the rationale template used in the ablation study ($ 3.3). For simplicity, we use the same prompt structure (Table 7) for all tasks with minor differences in task-specific instructions (Table 8).
Table 6: Rationale template used in ablation study.
Rationale Template
After reviewing provided document, found that only documents {documents} contain relevant information to answer the question. Based on my knowledge and the provided contents, the answer is: [answer].
Positive Template:
Negative Template: After reviewing the provided document, found that of them contain rel- evant information to answer the question. Based on my knowledge and the provided contents, answer is: (answer).
Table 7: Rationale generation prompt template.
Rationale Generation
Input: <
begin_offtext > start. _headerhhedrrrrrr _idd] >user< heachheace
Read the following documents relevant the given question: {question)
Document [1] (Title: ...): [contents]
Please identify documents that useful to answer the given question: "[guestion)". and explain how the contents lead the answer: (answer).
If none of the documents is aligned with the answer, in that case, have to explain the answer only based on your knowledge, without referring the provided information.
(task-specific instruction] <lottid start header aassistantte end header
Output: {rationale}
Table Task-specific instruction used in rationale generation prompt.
Task-specific Instruction for Rationale Generation
Note that the question may be ambiguous and have multiple correct answers. Make sure your response includes all correct answers and provides clear reasoning details followed by concise conclusion.
ASQA:
Note that question mainly asks about the object entity that holds certain relationship with the given subject entity. There may be multiple correct answers. Make sure your response includes all correct answers and provides clear reasoning details followed by concise conclusion.
PopQA:
TriviaQA / Natural Questions 2WikiMultiHooQA: Note that question may compositional require intermediate analysis to deduce the final answer. Make sure your response grounded and provides clear reasoning details followed by a concise conclusion.
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Inference prompts. Below present the inference prompts for both training-free and trainable RAG methods used in this work, including in-context RALM (Table 9), few-shot demonstrations with instruction (Table 10), and vanilla supervised fine-tuning (Table 11). Note that for fair com- parison, the inference prompt for our INSTRCCTAA--FT is exactly the same as vanilla SFT. Simi- larly, the inference prompt for INSTRUCTR AG-ICL shares the same inference instruction as the few- shot demonstrations with instruction. The only difference between the prompts of these two methods employs denoising question-rationale (g.
lies in the demonstrations where pairs, while shot demonstrations with instruction uses plain question-anse answer (g, a) pairs.
Table 9: Inference prompt for In-Context RALM.
In-Context RALM
Input: < (wbeeinnoo..... wfetett/////oooooooooo.ooooooele >x//start.headerraaao. > snn.eeeeeeeeeeheciiiiiil
Document [1] (Title: ..);; [contents]
Based on your knowledge and the provided information, answer the question: {question}
<lect id >ststart header id bassis innseitttttt < end header i
Output: (answer)
Table 10: Inference prompt for INSTRUCTRAG-ICL and few-shot demonstrations with instruction.
INSTRUCTRAG-ICL / Few-shot Demonstrations with instruction
Input: < l'begin of text >>< start: header id >user< end header i
Your task is to analyze the provided documents and answer the given question. Please generate a brief explanation of how the contents of these documents lead to your answer. If the provided information is not helpful answer the question, you only need respond based your own knowledge, without referring the documents.
Below are some examples of how to answer the question:
fexample question }
(example
rationale r
Document [1] (Title: ...): [contents]
Based your knowledge and the provided information, answer the question: {question) < iid
<lectiid] start htt.:
Output: (answer)
Table 11: Inference prompt for INSTRUCTRAG-FT and vanilla supervised fine-tuning.
INSTRUCTRAG-FT Vanilla Supervised Fine-tuning
Input:
< www.ienooooffoooooeeeoooo
Document [1] (Title: ...); {contents}
Based on your knowledge and the provided information, answer the question: {question)
<lect.id > start
header statt >aslaasssss bassi wasssssttttss end header.id
Output: (answer)
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E EXAMPLE OF LLM-AS-A-JUDGE
To measure the quality of mooel--gneerted rationales, we employ the jude approach for more comprehensive evaluation of the model's outputs. As illustrated Table 12, the rationale is inaccurate despite the final answer being correct (probably due to the use of the LLM's parametric knowledge), the LLM judge will detect this inconsistency.
Table 12: Evaluate rationale with LLM-as-a-judge.
Evaluation with LLM-as-a-judge
Input: < of text st<|start_ header _irdd >user< end www.he..erero | You question-answering evaluator. Given the question, please judge whether the rationale aligns with the true answer.
("question": "In what city was Catherine Tishem born?". "true answer": "Norwich, England",
"rationale": "The documents that are useful answer question are: Documents 4 and Document states that Catherine Tishem was from Breda, and her husband Wouter Gruter co-signed the Compromise of Nobles in 1566, and they moved to the Dutch Calvinist exile community of Norwich. This implies not horn in Norwich. Document 5 provides more information about Catherine erudite woman from Antwerp who educated her son, Jan Gruter,
that Catherine Tishem Tishem's life, stating that she while exile in England. This implies that Catherine Tishem was born in Antwerp. The other documents do not provide any information about Catherine Tishem's birthplace, so they are not useful for answering this question. Based on the above information, the answer is Norwich."] < <ot__id starttteadeee.iie....
ass stant end.header.idl)
The predicted answer concludes that Catherine Tishem was born in Norwich, which con- tradicts the conclusion drawn from the reasoning in the documents, where Antwerp is suggested as her birthplace based on the information in Document 5. Thus, while the predicted conclusion is Norwich, the rationale suggests Antwerp the likely birthplace based the information the documents. Therefore, the predicted answer does not align with true answer.
Output:
24


Reviews:
[{"review_id": "VfROiZl9Qf", "reviewer": "Reviewer_PDdA", "summary": "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.", "questions": "The questions can be viewed in the weakness", "limitations": "", "rating": 8, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "1. InstructRAG is a simple but effective method to denoise retrvied information and justify its predicted final answers by generating denoising reponse \n2. InstructRAG does not require any additional supervision and can be applied to both in-context learning and supervised fine-tuning settings \n3. good presentation and  detailed experiment", "weaknesses": "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. \n2. 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.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "FIA3AxDnXz", "reviewer": "Reviewer_caq8", "summary": "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.", "questions": "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:\n1. 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.\n2. 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.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 4, "contribution": 3, "strengths": "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.", "weaknesses": "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.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "8ZjSK4b8Dl", "reviewer": "Reviewer_GKWS", "summary": "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.", "questions": "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.", "limitations": "", "rating": 5, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 1, "strengths": "1. The method effectively leverages existing data, reducing the need for costly new annotations, which often involve extensive human effort.\n\n2. Demonstrating consistent outperformance over traditional RAG methods across multiple benchmarks indicates effectiveness.", "weaknesses": "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.\n\n2. 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. \n\n3. 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.\n\n4. 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.\n\n5. 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.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "0I4zUttZWy", "reviewer": "Reviewer_yPGP", "summary": "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.", "questions": "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?", "limitations": "", "rating": 8, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "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.\n2. This paper uses ablation experiments to prove that providing true answers and retrieved documents is very important for inference generation.\n3. 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.\n4. Through out-of-distribution (OOD) experiments, the authors demonstrate that both InstructRAG-ICL and InstructRAG-FT can generalize well to unseen tasks.", "weaknesses": "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?\n2. 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?\n3. 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?\n4. 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?", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "9nJTgt4HFs", "reviewer": "Reviewer_MKpj", "summary": "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:\n\n1. The approach is well-founded, as noise problems are prevalent in real-world RAG scenarios. InstructionRAG effectively addresses this problem.\n2. 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.\n3. The experiments are comprehensive, with validation across five datasets, demonstrating the effectiveness of InstructionRAG.\n\nHowever, 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.\n\nOverall, I am quite satisfied with the InstructionRAG method, which represents an influential contribution to the RAG field.", "questions": "Please refer to the Summary section.", "limitations": "", "rating": 8, "confidence": 4, "soundness": 4, "presentation": 4, "contribution": 3, "strengths": "Please refer to the Summary section.", "weaknesses": "Please refer to the Summary section.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)