mlsanityData to 14 Oct 2026

How Much Can RAG Help the Reasoning of LLM?

arXiv 2410.02338 ↗First posted 3 Oct 2024cs.CL cs.AIcited by 31

On the map: LLM reasoning → Knowledge- and tool-augmented reasoning

also in 1 more
Abstract (arXiv)

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.

Key facts. Cited by 31 papers in the map (data through 14 Oct 2026). Placed in Knowledge- and tool-augmented reasoning, one of 17 directions in LLM reasoning. Builds on 37 of its 40 references that are in the map. First posted on arXiv 3 Oct 2024.

Builds on

37 of 40 references.

References · 37

  1. 2020Language Models are Few-Shot Learnerscited by 32,419
  2. 2022Training language models to follow instructions with human feedbackcited by 15,971
  3. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,459
  4. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,297
  5. 2023Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflectioncited by 1,839
  6. 2023A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questionscited by 1,601
  7. 2022Atlas: Few-shot Learning with Retrieval Augmented Language Modelscited by 952
  8. 2023Large Language Models Can Be Easily Distracted by Irrelevant Contextcited by 841
  9. 2023A Simple and Effective Pruning Approach for Large Language Modelscited by 800
  10. 2023REPLUG: Retrieval-Augmented Black-Box Language Modelscited by 792
  11. 2023LongLLMLingua: Accelerating and Enhancing LLMs in Long Context Scenarios via Prompt Compressioncited by 453
  12. 2023Making Retrieval-Augmented Language Models Robust to Irrelevant Contextcited by 369
25 more
  1. 2024Retrieval-Augmented Generation for AI-Generated Content: A Surveycited by 354
  2. 2024LLMLingua-2: Data Distillation for Efficient and Faithful Task-Agnostic Prompt Compressioncited by 348
  3. 2023Towards Revealing the Mystery behind Chain of Thought: A Theoretical Perspectivecited by 294
  4. 2023LLMLingua: Compressing Prompts for Accelerated Inference of Large Language Modelscited by 287
  5. 2024The Power of Noise: Redefining Retrieval for RAG Systemscited by 281
  6. 2024RAFT: Adapting Language Model to Domain Specific RAGcited by 262
  7. 2024Chain of Thought Empowers Transformers to Solve Inherently Serial Problemscited by 260
  8. 2023Language Modeling Is Compressioncited by 225
  9. 2024Corrective Retrieval Augmented Generationcited by 197
  10. 2022The Parallelism Tradeoff: Limitations of Log-Precision Transformerscited by 193
  11. 2023Navigate through Enigmatic Labyrinth A Survey of Chain of Thought Reasoning: Advances, Frontiers and Futurecited by 184
  12. 2023Representational Strengths and Limitations of Transformerscited by 133
  13. 2023The Expressive Power of Low-Rank Adaptationcited by 102
  14. 2024Bias and Unfairness in Information Retrieval Systems: New Challenges in the LLM Eracited by 98
  15. 2023How Does Information Bottleneck Help Deep Learning?cited by 93
  16. 2023White-Box Transformers via Sparse Rate Reductioncited by 80
  17. 2024Compression Represents Intelligence Linearlycited by 56
  18. 2024Fine Tuning vs. Retrieval Augmented Generation for Less Popular Knowledgecited by 50
  19. 2024How Easily do Irrelevant Inputs Skew the Responses of Large Language Models?cited by 44
  20. 2023When Do Prompting and Prefix-Tuning Work? A Theory of Capabilities and Limitationscited by 43
  21. 2024Memory3: Language Modeling with Explicit Memorycited by 40
  22. 2019An Introductory Guide to Fano's Inequality with Applications in Statistical Estimationcited by 31
  23. 2024Prompting a Pretrained Transformer Can Be a Universal Approximatorcited by 25
  24. 2024A Theory for Token-Level Harmonization in Retrieval-Augmented Generationcited by 7
  25. 2024Investigating the performance of Retrieval-Augmented Generation and fine-tuning for the development of AI-driven knowledge-based systemscited by 4

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 5; 2025: 17; 2026: 9.

31 papers.

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  1. 2025Search-o1: Agentic Search-Enhanced Large Reasoning Modelscited by 433
  2. 2025WebThinker: Empowering Large Reasoning Models with Deep Research Capabilitycited by 335
  3. 2024ZoomEye: Enhancing Multimodal LLMs with Human-Like Zooming Capabilities through Tree-Based Image Explorationcited by 103
  4. 2025EraRAG: Efficient and Incremental Retrieval Augmented Generation for Growing Corporacited by 15
  5. 2025Vendi-RAG: Adaptively Trading-Off Diversity And Quality Significantly Improves Retrieval Augmented Generation With LLMscited by 14
  6. 2024Can LLMs be Good Graph Judge for Knowledge Graph Construction?cited by 11
  7. 2025RARE: Retrieval-Augmented Reasoning Modelingcited by 11
  8. 2025Do not Abstain! Identify and Solve the Uncertaintycited by 11
  9. 2024Rethinking Chain-of-Thought from the Perspective of Self-Trainingcited by 10
  10. 2025Towards Interactive Intelligence for Digital Humanscited by 10
  11. 2025RAG-KG-IL: A Multi-Agent Hybrid Framework for Reducing Hallucinations and Enhancing LLM Reasoning through RAG and Incremental Knowledge Graph Learning Integrationcited by 9
  12. 2024LLM-Mixer: Multiscale Mixing in LLMs for Time Series Forecastingcited by 8
19 more
  1. 2026LLMs Should Express Uncertainty Explicitlycited by 6
  2. 2025Optimizing Multi-Hop Document Retrieval Through Intermediate Representationscited by 5
  3. 2025ARise: Towards Knowledge-Augmented Reasoning via Risk-Adaptive Searchcited by 5
  4. 2026DF-RAG: Query-Aware Diversity for Retrieval-Augmented Generationcited by 5
  5. 2026Taxonomy of the Retrieval System Framework: Pitfalls and Paradigmscited by 4
  6. 2026Revisiting Chain-of-Thought Reasoning under Limited Supervision: Semi-supervised Chain-of-Thought Learningcited by 4
  7. 2025Multimodal RAG-driven Anomaly Detection and Classification in Laser Powder Bed Fusion using Large Language Modelscited by 3
  8. 2026Failure Modes in Multi-Hop QA: The Weakest Link Effect and the Recognition Bottleneckcited by 3
  9. 2024DateLogicQA: Benchmarking Temporal Biases in Large Language Modelscited by 2
  10. 2026RAG over Thinking Traces Can Improve Reasoning Taskscited by 2
  11. 2025Retrieval Augmented Learning: A Retrial-based Large Language Model Self-Supervised Learning and Autonomous Knowledge Generationcited by 1
  12. 2025CAMA: Enhancing Mathematical Reasoning in Large Language Models with Causal Knowledgecited by 1
  13. 2025A Lightweight Framework for Trigger-Guided LoRA-Based Self-Adaptation in LLMscited by 1
  14. 2026MemBoost: A Memory-Boosted Framework for Cost-Aware LLM Inferencecited by 1
  15. 2026The Structural Attention Tax: How Retrieval Format Hijacks In-Context Learning Independent of Contentcited by 1
  16. 2026Learning to Reason by Analogy via Retrieval-Augmented Reinforcement Fine-Tuningcited by 1
  17. 2025Leaps Beyond the Seen: Reinforced Reasoning Augmented Generation for Clinical Notescited by 0
  18. 2025ECG-LLM -- training and evaluation of domain-specific large language models for electrocardiographycited by 0
  19. 2025Living the Novel: A System for Generating Self-Training Timeline-Aware Conversational Agents from Novelscited by 0

Similar, not cited

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  1. 2025Synergizing RAG and Reasoning: A Systematic Review
  2. 2024Long-Context LLMs Meet RAG: Overcoming Challenges for Long Inputs in RAG
  3. 2024A Hybrid RAG System with Comprehensive Enhancement on Complex Reasoning
  4. 2024Retrieval Augmented Generation (RAG) and Beyond: A Comprehensive Survey on How to Make your LLMs use External Data More Wisely
  5. 2024Unveiling and Consulting Core Experts in Retrieval-Augmented MoE-based LLMs
  6. 2023Retrieval-Augmented Generation for Large Language Models: A Survey
  7. 2025RAG+: Enhancing Retrieval-Augmented Generation with Application-Aware Reasoning
  8. 2025Towards Agentic RAG with Deep Reasoning: A Survey of RAG-Reasoning Systems in LLMs