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.

翻译：检索增强生成（RAG）因其在引入新知识和减少幻觉方面的有效性，在现代大型语言模型（LLM）中获得了显著的普及。然而，对RAG的深入理解仍然有限，RAG如何帮助推理过程以及RAG是否能帮助提升推理能力仍是未解之谜。虽然外部文档通常被视为融入领域特定信息的一种方法，但它们也包含与查询相关的中间推理结果，这表明文档可以增强LLM的推理能力，这一点此前尚未被探索。在本文中，我们深入研究了这一问题，发现虽然RAG可以辅助推理，但这种帮助是有限的。如果我们将推理过程概念化为一个固定深度的树，那么RAG难以帮助LLM进行更深层次的推理。此外，文档中的信息需要预处理以过滤噪声。我们证明，这种预处理难以通过简单地微调LLM来实现，通常需要大量额外的Transformer层来解决该问题。为了简化问题，我们提出了DPrompt调优方法，该方法仅用有限的Transformer层就能有效解决此问题，从而带来性能提升。