发表机构
Hong Kong Polytechnic University; Nanjing University of Science and Technology; XU Exponential University of Applied Sciences; Vrije Universiteit Amsterdam（香港理工大学; 南京理工大学; XU应用科学大学; 阿姆斯特丹自由大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出关系感知情感支持对话任务及RESCUE基准，评估大模型在多方对话中理解关系动态并提供支持的能力，实验发现现有模型在关系密集型任务上表现不足。
AI 中文摘要
现有情感支持对话系统主要关注一对一求助者与支持者之间的互动以及个体情感状态，对多方场景中的人际关系探索不足。在本工作中，我们引入了关系感知的情感支持对话这一新任务，用于评估大语言模型（LLMs）能否捕捉并利用不断演变的关系动态来提供更有效的情感支持。我们从真实的夫妻及家庭访谈对话中构建了RESCUE（关系感知情感支持对话理解与评估基准），包含191个样本、7,079个标注轮次以及1,064.8分钟的视频。基于对社会情感和支持相关动态的丰富标注，RESCUE定义了六项任务，评估关系感知情感支持所需的两项核心能力：关系理解与关系敏感支持。对十个大语言模型的实验表明，当前模型在依赖局部情感或干预线索的任务上表现相对较好，但在关系密集型任务（如关系模式预测、观点预测和支持策略预测）上表现不佳。这些发现揭示了当前大语言模型在建模人际关系和做出关系敏感支持决策方面的局限性。
英文摘要
Existing emotional support conversation systems mainly focus on one-on-one seeker-supporter interactions and individual emotional states, leaving interpersonal relations in multi-party scenarios underexplored. In this work, we introduce relation-aware emotional support conversation, a new task that evaluates whether LLMs can capture and utilize the evolving dynamics of relationships to offer more effective emotional support. We construct RESCUE (Relation-aware Emotional Support Conversation Understanding and Evaluation Benchmark) from real couple and family interview conversations, containing 191 samples, 7,079 annotated turns, and 1,064.8 minutes of video. Based on rich annotations of socio-emotional and support-related dynamics, RESCUE defines six tasks that evaluate two core capabilities required for relation-aware emotional support: Relational Understanding and Relation-Sensitive Support. Experiments with ten LLMs show that current models perform relatively well on tasks relying on local emotional or intervention cues, but struggle with relation-intensive tasks such as relation pattern prediction, viewpoint prediction, and support strategy prediction. These findings reveal the limitations of current LLMs in modeling interpersonal relations and making relation-sensitive support decisions.
Commentsaccepted as AACL findings
