审计心理健康对话中的隐性谄媚：结构化临床状态诊断与干净匹配基准
Auditing Stealth Sycophancy in Mental-Health Dialogue: Structured Clinical-State Diagnostics and Clean Matched Benchmarks
AI总结：
针对心理健康对话模型中隐式谄媚（表面共情但强化消极认知）的问题，提出基于动态情感签名图（DESG）的结构化离线审计框架，通过临床状态转移评估响应方向，并在干净匹配基准上实现最优有害风险检测。
摘要：
心理健康对话模型越来越多地由基于AI的评估器进行评估，但这些评估器通常将表面共情、支持性或流畅性视为安全的证据。在本文中，我们研究了一种隐藏的失败模式，称为隐式谄媚：一个响应可能看似共情，但暗中强化灾难化、回避、绝望预测或CBT式标签。为了检查这个问题，我们引入了一个用于隐式谄媚检测的诊断基准，该基准基于三个代表性的心理健康对话来源构建，涵盖日常同伴支持、咨询式情感支持和危机导向互动，并进一步构建了一个泄漏审计的干净单响应匹配基准，包含500个上下文和1500个匹配响应窗口。然后，我们提出了动态情感签名图（DESG），一个结构化的离线审计框架，将基于LLM的状态提取与最终评分分离，并通过语义、情感和认知扭曲状态转移而非自由形式的LLM判断来评估临床方向。与元数据、表面风格、词汇、嵌入和基于规则的LLM基线不同，DESG对响应引起的临床状态变化方向进行评分；在泄漏审计的干净匹配基准上，DESG-StateRisk比最强的非DESG基线提高了0.0488 macro-F1，并实现了最佳的有害风险检测结果。这些结果表明，评估隐式谄媚需要显式的临床状态建模以及泄漏检查、捷径控制和竞争性基线。
英文摘要：
Mental-health dialogue models are increasingly evaluated by AI-based evaluators, yet these evaluators often treat surface empathy, supportiveness, or fluency as evidence of safety. In this paper, we study a hidden failure mode that we call implicit sycophancy: a response may appear empathetic while implicitly reinforcing catastrophizing, avoidance, hopeless prediction, or CBT-style labeling. To examine this problem, we introduce a diagnostic benchmark for implicit-sycophancy detection, built from three representative mental-health dialogue sources covering everyday peer support, counseling-style emotional support, and crisis-oriented interaction, and further construct a leakage-audited clean single-response matched benchmark with 500 contexts and 1,500 matched response windows. We then propose Dynamic Emotional Signature Graphs (DESG), a structured offline audit framework that separates LLM-based state extraction from final scoring and evaluates clinical direction through semantic, affective, and cognitive-distortion state transitions rather than free-form LLM judgment. Unlike metadata, surface-style, lexical, embedding, and rubric-LLM baselines, DESG scores the direction of clinical-state change induced by a response; on the leakage-audited clean matched benchmark, DESG-StateRisk improves over the strongest non-DESG baseline by 0.0488 macro-F1 and achieves the best harmful-risk detection result. These results suggest that evaluating implicit sycophancy requires explicit clinical-state modeling together with leakage checks, shortcut controls, and competitive baselines.
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。
