How Much Can RAG Help the Reasoning of LLM?

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. Large Language Models (LLMs) (Brown, 2020) have demonstrated remarkable capabilities across a variety of tasks, including text generation and question answering (Ouyang et al., 2022; Wei et al., 2022), code generation (Gu, 2023), and information retrieval (Dai et al., 2024). However, current LLMs often suffer from serious hallucinations (Huang et al., 2023) due to a lack of factual information. Moreover, the knowledge embedded within LLMs is encoded in their parameters (Yang et al., 2024), meaning that incorporating new knowledge requires further fine-tuning, which is both time-consuming and resource-intensive.