Summary of Rag-star: Enhancing Deliberative Reasoning with Retrieval Augmented Verification and Refinement, by Jinhao Jiang et al.
RAG-Star: Enhancing Deliberative Reasoning with Retrieval Augmented Verification and Refinement
by Jinhao Jiang, Jiayi Chen, Junyi Li, Ruiyang Ren, Shijie Wang, Wayne Xin Zhao, Yang Song, Tao Zhang
First submitted to arxiv on: 17 Dec 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 proposes a novel approach called RAG-Star, which integrates retrieved information to guide deliberative reasoning processes in large language models (LLMs). The method leverages Monte Carlo Tree Search to iteratively plan sub-queries and answers based on the LLM’s internal knowledge. To consolidate internal and external knowledge, the authors suggest retrieval-augmented verification using query- and answer-aware reward modeling. Experimental results show that RAG-Star outperforms previous RAG and reasoning methods on tasks such as problem-solving and complex reasoning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary RAG-Star is a new way to help big language models think more clearly. Right now, these models are great at solving some problems, but they can struggle with harder thinking tasks. This paper shows how to make them better by using information from the internet and their own knowledge. It’s like having a conversation with someone who has access to lots of information. |
Keywords
» Artificial intelligence » Rag
