Summary of How Much Can Rag Help the Reasoning Of Llm?, by Jingyu Liu et al.
How Much Can RAG Help the Reasoning of LLM?
by Jingyu Liu, Jiaen Lin, Yong Liu
First submitted to arxiv on: 3 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper explores the effectiveness of Retrieval-Augmented Generation (RAG) in Large Language Models (LLMs), particularly its impact on reasoning processes. RAG has been shown to introduce new knowledge and reduce hallucinations, but its ability to improve reasoning capability remains unclear. The study investigates how RAG affects LLMs’ reasoning abilities, revealing that while it can assist with some tasks, it struggles to perform deeper reasoning. Additionally, the study highlights the need for preprocessing documents to filter out noise, which requires additional transformer layers. To simplify this problem, the authors propose DPrompt tuning, which resolves the issue within limited transformer layers, leading to improved performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how a special type of language model called Retrieval-Augmented Generation (RAG) helps computers understand and reason about information. RAG is good at introducing new ideas and reducing mistakes, but it’s not clear if it can really help computers think better. The study finds that while RAG can be helpful, it has limits when it comes to deeper thinking. It also shows that the documents used to train these models need to be cleaned up before they can be used effectively. To make this process easier, the authors suggest a new way of training called DPrompt tuning, which works better and is more efficient. |
Keywords
» Artificial intelligence » Language model » Rag » Retrieval augmented generation » Transformer
