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CheckRLM: Effective Knowledge-Thought Coherence Checking in Retrieval-Augmented Reasoning

A framework for improving the reliability of reasoning language models through error checking and correction.

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CheckRLM: Effective Knowledge-Thought Coherence Checking in Retrieval-Augmented Reasoning

By Dingling Xu, Ruobing Wang, Qingfei Zhao, Yukun Yan, Zhichun Wang, Daren Zha, Shi Yu, Zhenghao LiuarXiv
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CheckRLM proposes a framework to improve the reliability of reasoning language models by timely checking and correcting factual errors. It extracts claims from the reasoning chain, identifies inconsistencies, and performs minimal-cost corrections using external knowledge.

The framework demonstrates strong capability in mitigating error accumulation in long-horizon reasoning with lower costs.

Abstract

CheckRLM proposes a framework to improve the reliability of reasoning language models by timely checking and correcting factual errors. It extracts claims from the reasoning chain, identifies inconsistencies, and performs minimal-cost corrections using external knowledge. The framework demonstrates strong capability in mitigating error accumulation in long-horizon reasoning with lower costs.

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reasoning language modelsfactual error correctionretrieval-augmented generationknowledge consistency checkingerror accumulation mitigationLarge Language ModelsRetrieval & RAGSemantic InteroperabilityAI Agents
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