CheckRLM: 检索增强推理中的有效知识-思维连贯性检查
CheckRLM: Effective Knowledge-Thought Coherence Checking in Retrieval-Augmented Reasoning
AI总结：
提出CheckRLM框架，通过检索增强生成及时检查和纠正推理链中的事实错误，确保推理与知识一致，降低长程推理错误累积成本。
摘要：
推理语言模型（RLM）通过扩展推理链在复杂任务上取得了显著性能提升。然而，这些推理链容易包含事实错误，尤其是在知识密集型任务中。为解决这一问题，我们提出CheckRLM，一个通过检索增强生成（RAG）及时检查和纠正事实错误以提高推理过程可靠性的框架。具体而言，CheckRLM从推理链中提取事实声明，以识别和定位推理过程中的细微知识不一致性。一旦检测到错误，精炼机制通过利用外部知识进行最小成本但精确的修正，确保推理链与正确知识之间的连贯性。大量实验表明，CheckRLM显著优于现有基线，展现出以更低成本缓解长程推理中错误累积的强大能力。代码和数据可在该https URL获取。
英文摘要：
Reasoning Language Models (RLMs) have significantly improved performance on complex tasks by extending the reasoning chain. However, these chains are prone to containing factual errors, particularly in knowledge-intensive tasks. To address this issue, we propose CheckRLM, a framework that improves the reliability of the reasoning process through Retrieval-Augmented Generation (RAG) by timely checking and correcting factual errors. Specifically, CheckRLM extracts factual claims from the reasoning chain to identify and localize subtle knowledge inconsistencies during inference. Upon detection of errors, a refinement mechanism performs minimal-cost yet precise corrections by leveraging external knowledge, ensuring coherence between the reasoning chain and correct knowledge. Extensive experiments demonstrate that CheckRLM substantially outperforms existing baselines, exhibiting a strong capability to mitigate error accumulation in long-horizon reasoning with lower costs. The code and data are available at https://github.com/AI9Stars/CheckRLM.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
