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arXiv 2603.02876cs.CL

Eval4Sim: 一种用于人设模拟的评估框架

Eval4Sim: An Evaluation Framework for Persona Simulation

University of Sheffield(谢菲尔德大学)

Eliseo Bao, Anxo Perez, Javier Parapar, Xi Wang

AI总结:

Eval4Sim提出了一种评估框架,用于衡量模拟对话与人类对话模式的契合度,通过三个互补维度评估人设的忠实性、一致性和自然性。

摘要:

大型语言模型(LLM)的人设,具有明确的属性、背景和行为倾向的规范,越来越多地被用于模拟人类对话,以完成用户建模、社交推理和行为分析等任务。因此,确保基于人设的模拟能够忠实反映人类对话行为至关重要。然而,当前的评估实践大多依赖于LLM作为判断者的方法,缺乏对可观察人类行为的支撑,并产生模糊的标量分数。我们通过提出Eval4Sim评估框架来填补这一空白,该框架测量模拟对话在三个互补维度上与人类对话模式的契合程度。一致性衡量一个角色在不同对话中是否保持可辨识的身份,通过作者身份验证计算。自然性反映对话是否表现出人类般的流畅性,而不是过于僵硬或优化的结构,通过对话聚焦的自然语言推理分布来量化。与绝对或优化导向的指标不同,Eval4Sim使用人类对话语料库(即PersonaChat)作为参考基线,并对双向偏差进行惩罚,区分不足的人设编码与过度优化、不自然的行为。尽管在PersonaChat上进行了演示,Eval4Sim的应用范围扩展到任何包含说话者层面注释的对话语料库。

英文摘要:

Large Language Model personas, explicit profiles specifying a user's attributes, preferences, and behavioural tendencies, are increasingly used to simulate human conversations for user modelling, social reasoning, and behavioural analysis. Evaluating whether such simulations faithfully reflect human conversational behaviour is critical, yet current practice often relies on LLM-as-a-judge approaches that provide limited grounding in observable behaviour and produce opaque scalar scores. We present Eval4Sim, an evaluation framework that measures alignment between simulated and human conversations across three dimensions: adherence, whether persona traits are recoverable from dialogue via dense retrieval; consistency, whether a persona maintains a distinguishable stylistic identity via authorship verification; and naturalness, whether conversations exhibit human-like turn-to-turn flow via dialogue NLI. Unlike optimization-oriented metrics, each dimension takes a human corpus as a reference baseline and penalizes deviations in both directions, distinguishing insufficient persona encoding from over-optimized, unnatural behaviour. The framework is corpus-agnostic: any persona-annotated conversational dataset can serve as the reference. Evaluated over ten simulation corpora, Eval4Sim surfaces systematic trade-offs invisible to single-score methods.

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。

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