RESCUE-BENCH：邁向關係感知的多方情緒支持對話系統

摘要

現有的情緒支持對話系統主要聚焦於一對一尋求者與支持者之間的互動及個別情緒狀態，對於多方情境中的人際關係則較少探討。在本研究中，我們提出關係感知情緒支持對話，這是一項新任務，用以評估大型語言模型是否能捕捉並運用關係的演變動態，以提供更有效的情緒支持。我們從真實伴侶與家庭訪談對話建構 RESCUE（Relation-aware Emotional Support Conversation Understanding and Evaluation Benchmark），包含 191 個樣本、7,079 個標註對話輪次，以及 1,064.8 分鐘影片。基於社會情緒與支持相關動態的豐富標註，RESCUE 定義六項任務，以評估關係感知情緒支持所需的兩項核心能力：關係理解與關係敏感支持。針對十個大型語言模型的實驗顯示，當前模型在依賴局部情緒或介入線索的任務上表現相對良好，但在關係密集型任務（如關係模式預測、觀點預測與支持策略預測）上仍有困難。這些發現揭示當前大型語言模型在人際關係建模以及做出關係敏感支持決策方面的限制。

English

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.