用语义熵量化模型推理链与人类偏好对齐程度，发现两步推理最契合。
Chain-of-Thought as a Lens: Evaluating Structured Reasoning Alignment between Human Preferences and Large Language Models
- 构建基于语义熵的矩阵，对比模型推理链与人类参考路径
- 2跳推理时对齐度最高，深度增加后错位主要因主题偏移和冗余
- 对齐分数能有效预测准确率、可读性与连贯性，适合作为诊断工具
本文提出一种量化评估大语言模型多步结构化推理与人类偏好对齐程度的方法。引入对齐得分（Alignment Score），通过在中间步骤构建语义熵矩阵，比较模型生成的思维链与人类偏好参考路径的差异。分析表明，对齐得分在不同模型和推理深度下与任务准确率高度相关，峰值出现在2跳推理。实证结果进一步显示，深层推理中的错位主要由主题偏移和冗余推理等对齐错误驱动。将链路采样视为从推理路径分布中抽样，我们证实对齐得分与准确率、可读性和连贯性存在强而稳定的关联，支持其作为诊断信号的适用性。代码已开源。
原文摘要 · Abstract (English)
This paper primarily demonstrates a method to quantitatively assess the alignment between multi-step, structured reasoning in large language models and human preferences. We introduce the Alignment Score, a semantic-level metric that compares a model-produced chain of thought traces with a human-preferred reference by constructing semantic-entropy-based matrices over intermediate steps and measuring their divergence. Our analysis shows that Alignment Score tracks task accuracy across models and hop depths, and peaks at 2-hop reasoning. Empirical results further indicate that misalignment at greater reasoning depths is driven mainly by alignment errors such as thematic shift and redundant reasoning. Viewing chain sampling as drawing from a distribution over reasoning paths, we empirically demonstrate a strong and consistent correlation between Alignment Score and accuracy, readability, and coherence, supporting its use as a diagnostic signal. The code is available.
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