研究推理表示对人类评估大模型输出的帮助，发现好看不等于好用。
Do Reasoning Representations Help Humans Evaluate LLM Outputs?
- 把推理过程当人看的界面，测试六种格式在不同任务中的表现。
- 简单思维链更利于验证、信任和可解释性，但人更喜欢复杂格式。
- 偏好复杂格式易导致误判，高信任却不愿核对，有风险。
推理表示被越来越多地用作大语言模型输出的解释，但其评估通常基于模型中心指标（如答案准确率和忠实度），未明确其是否真正帮助人类评估模型响应。本文将推理表示视为面向人类的交互界面，而非模型推理能力的代理。我们通过一个受控的人类实验，在不同复杂度的任务中对比六种推理格式，依托基于网页的框架随机化任务领域、问题实例和表示顺序。实验收集了关于结构理解、错误检测与定位、信任校准的细粒度判断。结果表明，人类偏好与实际支持存在错位：参与者更青睐基于规划和分解的表示形式，但更简单的思维链（Chain-of-Thought）痕迹在支持验证、信任建立和可解释性方面表现更优。此外，偏好格式带来校准风险——对正确轨迹产生更多误报，且即使不愿验证也表现出高信任度。
原文摘要 · Abstract (English)
Reasoning representations are increasingly used as explanations for large language model outputs. Yet they are typically evaluated with model-centric criteria, such as answer accuracy and faithfulness, leaving it unclear whether they help people evaluate model responses. In this work, we study reasoning representations as human-facing interfaces rather than proxies for model reasoning ability. We conduct a controlled human study of six reasoning formats across tasks of varying complexity, supported by a web-based framework that randomizes task domains, problem instances, and representation order. The study collects fine-grained judgments of structural understanding, error detection and localization, and trust calibration. Our study shows a mismatch between perceived preference and support for human evaluation. Participants prefer planning- and decomposition-based representations, but simpler chain-of-thought traces better support verification, trust, and interpretability. Preferred representations also introduce calibration risks, with more false alarms on correct traces and high trust despite low willingness to verify.
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