Qichun Deng

Zhouxing Tan, Ruochong Xiong, Yulong Wan et al.

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.

Haifeng Jing, Yujie Hou, Junfei Liu et al.

With the rapid development of Large Language Models, dialogue systems are shifting from information tools to emotional companions, heralding the era of Emotional Companionship Dialogue Systems (ECDs) that provide personalized emotional support for users. However, the field lacks clear definitions and systematic evaluation standards for ECDs. To address this, we first propose a definition of ECDs with formal descriptions. Then, based on this theory and the design principle of "Ability Layer-Task Layer (three level)-Data Layer-Method Layer", we design and implement the first ECD evaluation benchmark - MoodBench 1.0. Through extensive evaluations of 30 mainstream models, we demonstrate that MoodBench 1.0 has excellent discriminant validity and can effectively quantify the differences in emotional companionship abilities among models. Furthermore, the results reveal current models' shortcomings in deep emotional companionship, guiding future technological optimization and significantly aiding developers in enhancing ECDs' user experience.