EmoHarbor：通过模拟用户的内心世界评估个性化情感支持
EmoHarbor: Evaluating Personalized Emotional Support by Simulating the User's Internal World
AI总结：
EmoHarbor通过模拟用户内心世界，评估个性化情感支持的定制能力，揭示大语言模型在情感支持中的不足，推动用户感知情感支持系统的发展。
摘要：
当前用于评估情感支持对话的评估范式倾向于奖励通用的共情回应，却未能评估支持是否真正针对用户的独特心理特征和情境需求。我们引入EmoHarbor，一个自动化评估框架，通过模拟用户的内心世界采用用户作为评判者的方法。EmoHarbor采用了一种代理链架构，将用户的内部过程分解为三个专门的角色，使代理能够与支持者互动并以类似人类用户的方式完成评估。我们通过100个真实用户档案实例化此基准，这些档案涵盖了广泛的人格特征和情境，定义了10个个性化支持质量的评估维度。对20种先进的大语言模型在EmoHarbor上的全面评估揭示了一个关键发现：尽管这些模型擅长生成共情回应，但它们始终无法将支持定制到个体用户情境中。这一发现重新定义了核心挑战，将研究重点从单纯提升通用共情转向开发真正用户感知的情感支持。EmoHarbor提供了一个可重复和可扩展的框架，以指导更细致和用户感知的情感支持系统的发展和评估。
英文摘要：
Current evaluation paradigms for emotional support conversations tend to reward generic empathetic responses, yet they fail to assess whether the support is genuinely personalized to users' unique psychological profiles and contextual needs. We introduce EmoHarbor, an automated evaluation framework that adopts a User-as-a-Judge paradigm by simulating the user's inner world. EmoHarbor employs a Chain-of-Agent architecture that decomposes users' internal processes into three specialized roles, enabling agents to interact with supporters and complete assessments in a manner similar to human users. We instantiate this benchmark using 100 real-world user profiles that cover a diverse range of personality traits and situations, and define 10 evaluation dimensions of personalized support quality. Comprehensive evaluation of 20 advanced LLMs on EmoHarbor reveals a critical insight: while these models excel at generating empathetic responses, they consistently fail to tailor support to individual user contexts. This finding reframes the central challenge, shifting research focus from merely enhancing generic empathy to developing truly user-aware emotional support. EmoHarbor provides a reproducible and scalable framework to guide the development and evaluation of more nuanced and user-aware emotional support systems.
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。
