检测情绪动态轨迹：语言模型情绪支持能力的评估框架
Detecting Emotional Dynamic Trajectories: An Evaluation Framework for Emotional Support in Language Models
AI总结：
针对现有LLMs情绪支持评估依赖静态对话的局限，提出基于轨迹的评估框架，构建含328个情绪语境和1152个干扰事件的基准，引入三个轨迹指标，揭示模型情绪支持能力差异并为开发提供见解。
摘要：
情绪支持是人机交互的核心能力，应用场景包括心理咨询、角色扮演和陪伴。然而，现有大语言模型（LLMs）评估常依赖简短静态对话，无法捕捉情绪支持的动态和长期特性。为克服这一局限，我们从基于快照的评估转向基于轨迹的评估，采用以用户为中心的视角，根据模型随时间改善和稳定用户情绪状态的能力进行评估。我们的框架构建了包含328个情绪语境和1152个干扰事件的大规模基准，模拟对话场景演化下的真实情绪变化。为鼓励符合心理学原理的响应，我们使用情境选择、认知重评等经过验证的情绪调节策略约束模型输出。用户情绪轨迹被建模为一阶马尔可夫过程，我们应用因果调整情绪估计以获得无偏情绪状态跟踪。基于该框架，我们引入三个轨迹级指标：基线情绪水平（BEL）、情绪轨迹波动性（ETV）和情绪质心位置（ECP）。这些指标共同捕捉用户随时间的情绪动态，支持对LLMs长期情绪支持性能的全面评估。对多种LLMs的广泛评估揭示了情绪支持能力的显著差异，为模型开发提供了可操作的见解。
英文摘要：
Emotional support is a core capability in human-AI interaction, with applications including psychological counseling, role play, and companionship. However, existing evaluations of large language models (LLMs) often rely on short, static dialogues and fail to capture the dynamic and long-term nature of emotional support. To overcome this limitation, we shift from snapshot-based evaluation to trajectory-based assessment, adopting a user-centered perspective that evaluates models based on their ability to improve and stabilize user emotional states over time. Our framework constructs a large-scale benchmark consisting of 328 emotional contexts and 1,152 disturbance events, simulating realistic emotional shifts under evolving dialogue scenarios. To encourage psychologically grounded responses, we constrain model outputs using validated emotion regulation strategies such as situation selection and cognitive reappraisal. User emotional trajectories are modeled as a first-order Markov process, and we apply causally-adjusted emotion estimation to obtain unbiased emotional state tracking. Based on this framework, we introduce three trajectory-level metrics: Baseline Emotional Level (BEL), Emotional Trajectory Volatility (ETV), and Emotional Centroid Position (ECP). These metrics collectively capture user emotional dynamics over time and support comprehensive evaluation of long-term emotional support performance of LLMs. Extensive evaluations across a diverse set of LLMs reveal significant disparities in emotional support capabilities and provide actionable insights for model development.
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。
