# How Much Can RAG Help the Reasoning of LLM?

**Authors:** Jingyu Liu, Jiaen Lin, Yong Liu  
**arXiv:** [2410.02338](https://arxiv.org/abs/2410.02338)  
**Date:** 2024-10-03  
**Summary page:** https://www.summarizepaper.com/en/paper/2410.02338v1/

## Key points

- Retrieval-Augmented Generation (RAG) is a popular technique in Large Language Models (LLMs) for enhancing performance and reducing hallucinations.
- RAG's effectiveness in aiding reasoning processes and improving capabilities is limited.
- Leveraging external documents to incorporate domain-specific information can enhance LLMs' reasoning abilities, an area that has not been extensively explored.
- RAG struggles to facilitate deeper levels of reasoning in LLMs when conceptualizing the reasoning process as a tree with fixed depth.
- Preprocessing of information within documents is necessary to filter out noise, which proves challenging through simple fine-tuning of LLMs and often requires additional transformer layers.
- DPrompt tuning is introduced as a solution to address challenges within limited transformer layers and improve performance outcomes.

## Summary

In the realm of Large Language Models (LLMs), Retrieval-Augmented Generation (RAG) has emerged as a popular technique for enhancing performance and reducing hallucinations. However, the effectiveness of RAG in aiding reasoning processes and improving capabilities remains limited. While external documents are commonly used to incorporate domain-specific information, they also contain intermediate reasoning results related to the query. This suggests that leveraging documents could enhance LLMs' reasoning abilities, an area that has not been extensively explored. This paper delves into the intricacies of how RAG assists with reasoning in LLMs and uncovers that while it can provide some support, its effectiveness is constrained. When conceptualizing the reasoning process as a tree with fixed depth, RAG struggles to facilitate deeper levels of reasoning in LLMs. Additionally, the information within documents requires preprocessing to filter out noise - a task that proves challenging through simple fine-tuning of LLMs and often requires additional transformer layers to address effectively. To address these challenges and streamline the problem-solving process, this paper introduces DPrompt tuning as a solution. This novel approach effectively resolves issues within limited transformer layers and leads to improved performance outcomes. By shedding light on the limitations of RAG in aiding deeper reasoning processes and proposing innovative solutions like DPrompt tuning, this study contributes valuable insights into enhancing LLMs' reasoning capabilities for future advancements in natural language processing tasks.