RARE: Retrieval-Augmented Reasoning Enhancement for Large Language Models

• URL: http://arxiv.org/abs/2412.02830v4
• Date: Mon, 02 Jun 2025 17:40:21 GMT
• Title: RARE: Retrieval-Augmented Reasoning Enhancement for Large Language Models
• Authors: Hieu Tran, Zonghai Yao, Junda Wang, Yifan Zhang, Zhichao Yang, Hong Yu,
• Abstract summary: RARE (Retrieval-Augmented Reasoning Enhancement) is a versatile extension to the mutual reasoning framework (rStar)<n>It aims at enhancing reasoning accuracy and factual integrity across large language models (LLMs) for complex, knowledge-intensive tasks such as commonsense and medical reasoning.
• Score: 13.478123641238277
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: This work introduces RARE (Retrieval-Augmented Reasoning Enhancement), a versatile extension to the mutual reasoning framework (rStar), aimed at enhancing reasoning accuracy and factual integrity across large language models (LLMs) for complex, knowledge-intensive tasks such as commonsense and medical reasoning. RARE incorporates two innovative actions within the Monte Carlo Tree Search (MCTS) framework: A6, which generates search queries based on the initial problem statement, performs information retrieval using those queries, and augments reasoning with the retrieved data to formulate the final answer; and A7, which leverages information retrieval specifically for generated sub-questions and re-answers these sub-questions with the relevant contextual information. Additionally, a Retrieval-Augmented Factuality Scorer is proposed to replace the original discriminator, prioritizing reasoning paths that meet high standards of factuality. Experimental results with LLaMA 3.1 show that RARE enables open-source LLMs to achieve competitive performance with top open-source models like GPT-4 and GPT-4o. This research establishes RARE as a scalable solution for improving LLMs in domains where logical coherence and factual integrity are critical.

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