AI 中文总结
本研究基于成人依恋理论构建情感陪伴基准ECBench，评估32个LLM的依恋倾向，探究其在多轮交互中的表现及可调整性，为情感陪伴类LLM的选择提供理论与工具支持。
AI 中文摘要
随着大语言模型（LLM）越来越多地被应用于情感陪伴领域，评估其在亲密关系中的行为与能力已成为紧迫问题。然而，现有评估主要聚焦于通用人格特质，对模型在亲密且情感敏感情境下的行为揭示有限。因此，我们将成人依恋理论引入LLM评估，采用亲密关系体验量表修订版（ECR-R）来表征依恋焦虑与依恋回避。为评估LLM在现实交互场景中的情感陪伴能力，我们构建了情感陪伴基准ECBench，涵盖情感支持、协作任务、冲突解决、社交指导四类场景，涉及友谊与浪漫关系两种关系类型。使用11项对话质量指标和三种评估方法，通过ECBench评估了32个LLM的依恋倾向，并选取代表性模型探究这些倾向如何在情境化多轮交互中体现，以及是否可通过提示调整这些倾向。本研究提供了心理学理论视角，以及用于理解和选择情感陪伴类LLM的实用工具。
英文摘要
As large language models (LLMs) are increasingly applied for emotional companionship, evaluating their behavior and capabilities in intimate relationships has become a pressing issue. However, existing assessments primarily characterize general personality traits, providing limited insight into model behavior within intimate and emotionally sensitive contexts. Therefore, we introduce adult attachment theory into LLM evaluation and use the Experiences in Close Relationships-Revised (ECR-R) scale to characterize attachment anxiety and avoidance. To evaluate emotional companionship capabilities of LLMs in realistic interaction scenarios, we present an emotional companionship benchmark, ECBench, spanning four scenarios including emotional support, collaborative tasks, conflict resolution, and social guidance, across friendship and romantic relationships. ECBench is utilized to assess model behavior using 11 dialogue-quality metrics and three evaluation methods. We evaluate the attachment tendencies of 32 LLMs and select representative models to investigate how these tendencies manifest in contextualized multi-turn interactions and whether they can be shaped through prompting. Our study provides a theoretical lens from psychology, along with practical tools to understand and select LLMs for emotional companionship.
