How Much Can RAG Help the Reasoning of LLM?

Authors: Jingyu Liu, Linjiaen, Yong Liu
Primary area: causal reasoning
Keywords: RAG, Reasoning, LLM

Abstract:
Retrieval-Augmented Generation (RAG) has gained significant popularity in modern Large Language Models (LLMs) due to its effectiveness in introducing new knowledge and reducing hallucinations. However, the deep understanding of RAG remains limited, how does RAG help the reasoning process and can RAG help improve the reasoning capability remains question. While external documents are typically considered as a method to incorporate domain-specific information, they also contain intermediate reasoning results related to the query, this suggests that documents could enhance the reasoning capability of LLMs, which has not been previously explored. In this paper, we investigate this issue in depth and find that while RAG can assist with reasoning, the help is limited. If we conceptualize the reasoning process as a tree with fixed depth, then RAG struggles to assist LLMs in performing deeper reasoning. Additionally, the information in the documents requires preprocessing to filter out noise. We demonstrate that this preprocessing is difficult to achieve simply fine-tuning of the LLM, it often necessitates numerous additional transformer layers to solve the problem. To simplify the problem, we propose DPrompt tuning, which effectively resolves the issue within just limited transformer layers, leading to improved performance.

Decision: reject
Reviewer scores: 6, 3, 5

Reviews and discussion:
<thread>
creation date:
1730431310037

thread_type:
official_review

<thread_object>
creator: reviewer_v2it

<content>
summary:
- This paper demonstrated that RAG can enhance the reasoning ability of LLMs to a certain extent, but the improvement is limited.
- It highlights the challenges of simply fine-tuning models to filter out noise, and shows that filtering noise often requiring additional reasoning layers.
- This paper revealed the complexity of filtering noisy documents and proposed the DPrompt tuning method to simplify the problem.

strengths:
- This paper utilized the concept of reasoning trees to deeply investigate the extent to which RAG can assist in reasoning, and theoretically analyzed that RAG helps improve reasoning ability but also has limitations.
- This work analyzed the theoretical impact of different noise filtering methods on reasoning.
- To address the challenges of noise filtering, the authors proposed the DPrompt tuning method. This approach transforms complex ternary relation problems into more manageable binary relation problems, which is theoretically sound and has been empirically proven effective.

weaknesses:
1.	The authors theoretically demonstrate the extent to which RAG aids reasoning, but experimental evidence on reasoning tasks would be valuable to support these claims.
2.	Although the authors analyze the limitations of common noise filtering methods for noisy documents theoretically, experimental data is needed to make these arguments more robust.
3.	The experimental setup lacks clarity; methods in Table 1 should be accompanied by detailed explanations of the differences between relevant baselines.
4.	The Natural Questions (NQ) dataset typically contains relatively simple questions with low reasoning requirements, which somewhat diverges from the topic of the paper.
5.	This paper lacks an experimental setting to evaluate whether the model can reason effectively using only noisy documents in the context.
6.	While the paper emphasizes noise filtering, it lacks discussion of related noise filtering works, such as Wang et al.'s "Learning to filter context for retrieval-augmented generation" or the re-rank-based filter method by Glass et al. in "Re2G: Retrieve, rerank, generate."
7.	In line 740, "k" appears to be a typo and should actually be "n".

questions:
1.	In your modeling, you describe the role of RAG as reducing the depth of reasoning required for a given question probabilistically. Can it cover the conditions under which RAG mitigates hallucinations or corrects reasoning errors(where it may increase reasoning depth but help guide the model to the correct solution path)?
2.	All three models have been trained on chat data and are aligned, but the explanation given in lines 482-485 seems rather tenuous—could you provide a stronger justification?
3.	The training method in this paper only uses 1,000 samples, whereas other methods like self-RAG use tens of thousands. Could the weaker performance of the LoRA baseline in your experiments be due to insufficient data?
4.	How was the data constructed for baseline training? Did it include both gold and distractor documents?
5.	Why didn’t you use benchmarks for noise robustness, such as RGB(Chen, Jiawei, et al. "Benchmarking large language models in retrieval-augmented generation."), for evaluating RAG?
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</thread>
<thread>
creation date:
1730433133674

thread_type:
official_review

<thread_object>
creator: reviewer_mzlc

<content>
summary:
This paper analyzes the issues in using RAG to solve reasoning problems from the perspective of a reasoning tree.
It concludes that only when most intermediate reasoning results are retrieved can the reasoning depth be significantly reduced.
It also argues that the noise introduced in the retrieval stage cannot be resolved through fine-tuning.
Finally, the paper proposes a method that uses an additional model to condense the retrieved documents.

strengths:
The strengths of this paper lie in its formal modeling of the reasoning process. By exploring the issues in the reasoning process involving RAG under given assumptions, it decomposes RAG’s involvement into two parts: document processing and inference incorporation. It also provides insights into why RAG struggles to perfectly resolve the reasoning process. Additionally, the paper conducts an extensive analysis of how to overcome the noise in retrieved documents.

weaknesses:
The limitations of this paper include a lack of concrete basis for the assumptions used in modeling the reasoning process, as well as a lack of corresponding experiments to validate the modeling of noise effects. Additionally, the proposed method, DPrompt Tuning, shows limited innovation and is tested only on NQ, which does not have high requirements for multi-step reasoning (compared to the node depth used in the author’s modeling).

questions:
Q1: Why is the connection between different nodes in adjacent layers assumed to occur with probability `q` when defining the reasoning tree?

Q2: If a deterministic representation is used, causing f(t),  as expressed in Line 212, to no longer increase with respect to `t` , is the subsequent analysis still valid? (A simple scenario is when a node has three child nodes, with the first two having two child nodes each, and the last one having five. In this case, the erase rate does not exhibit a monotonic relationship.)

Q3: Di He et al. proved that CoT can enhance computational power through the “thought” portion, enabling the resolution of arbitrary DP problems. Additionally, other studies have shown that adding meaningless characters during reasoning can also increase computational capacity, thereby enhancing the model’s reasoning ability. Considering these findings simultaneously, whether they would impact the original conclusions.
</content>
</thread_object>

</thread>
<thread>
creation date:
1730657611470

thread_type:
official_review

<thread_object>
creator: reviewer_vhpc

<content>
summary:
The paper examines an unexplored aspect of Retrieval Augmented Generation (RAG) by investigating its potential to enhance LLMs' reasoning capabilities. While RAG is commonly used for incorporating domain knowledge through external documents, the authors make an interesting observation that these documents often contain intermediate reasoning steps. This leads to their core research question: whether RAG can improve LLMs' complex reasoning abilities. Their investigation reveals that RAG's assistance in reasoning is limited and requires careful document preprocessing to filter noise. The authors identify that this preprocessing challenge cannot be effectively addressed through simple LLM fine-tuning, as it typically demands multiple additional transformer layers. As a solution, they propose DPrompt tuning, which efficiently handles these challenges using minimal transformer layers and achieves better performance without extensive fine-tuning requirements.

strengths:
Strengths:

- Provides novel analysis of RAG capabilities
- Presents solid theoretical foundations supported by empirical evidence
- Introduces effective methods for noise filtering in RAG systems
- Clearly identifies limitations in fine-tuning approaches for noise filtering
- Develops an innovative prompting technique as a practical solution

weaknesses:
Weaknesses:

- Assumption 2.3 has notable limitations. When retrieved documents are relevant but significantly longer than the original query, or when information extraction requires multiple hops, the problem may not be simplified as claimed. This challenges the assumption that information extraction is simpler than layered reasoning paths, particularly considering LLM processing time.
- Limited experimental validation across domains, particularly lacking evaluation on multi-hop question answering tasks (e.g., HotpotQA) and long-form text generation.

- Missing References:

- - On noise filtering: https://arxiv.org/abs/2311.08377
- - On fine-tuning RAG models:
- - - https://arxiv.org/abs/2310.11511
- - - https://arxiv.org/abs/2410.01782

questions:
See weaknesses.
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</thread>


Metareview:
metareview:
This paper explores the intriguing question of whether retrieval augmented generation (RAG) can enhance the reasoning capabilities of large language models (LLMs). The authors investigate this hypothesis and propose a novel framework to leverage RAG for improved reasoning. Their experiments demonstrate the effectiveness of their method, surpassing alternative approaches like LoRA.

The paper addresses an interesting and under-explored area, presenting a clear and logical flow. However, its empirical support is limited.  As noted by other reviewers, more extensive experiments across diverse reasoning tasks are needed to validate the proposed method. While the authors provided additional experiments during the rebuttal, both the reviewers and I believe further evaluation is necessary.

Additionally, concerns exist regarding the assumptions made in the paper, which are considered strong and potentially impractical. Strengthening the empirical evaluation and relaxing these assumptions would significantly bolster the paper's position and contribution.