用图模型检测大模型推理链中的逻辑错误
LCoT-GV: Graph Attention Networks for Verifying Long Reasoning Chains in Large Language Models
- 将推理步骤构造成带语义关系的图结构
- 在多个数据集上验证准确率优于基线方法
- 适合需要高可信度推理的AI系统开发者
大型推理模型会产生包含多个推理步骤的长链式思考（LCoT），但这些步骤中常存在矛盾、无依据推断或无关内容，即使最终答案正确。我们提出长链式思考图验证器（LCoT-GV），一种基于图的框架，将LCoT表示为推理图：每个节点代表一个推理步骤，边编码语义与逻辑关系。使用图注意力网络从推理图中预测整个链的正确性。我们在多个问答领域构建了一个面向图结构的验证数据集。实验结果表明，该方法在性能上可与最相似的现有方法相媲美。
原文摘要 · Abstract (English)
Large Reasoning Models produce Long Chains-of-Thought (LCoTs) which involve breaking down the problem into smaller reasoning steps before reaching the conclusion. However, these steps often contain contradictions, unsupported inferences, or irrelevant steps, even when the final answer is correct. We propose Long Chain-of-Thought Graph Verifier (LCoT-GV), a graph-based framework that represents LCoTs as reasoning graphs. Each node in the graph represents a reasoning step and the edges encode semantic and logical relations. A Graph Attention Network is then trained to predict chain-of-thought correctness from the reasoning graph. We construct a new graph-oriented verification dataset from multiple reasoning benchmarks for question answering in various domains. The results show that our method is competitive with the most similar approaches.
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