LLMs真的能体现人类个性吗？分析AI与人类行为在纠纷解决中的对齐
Can LLMs Truly Embody Human Personality? Analyzing AI and Human Behavior Alignment in Dispute Resolution
AI总结：
本文研究LLMs在模拟人类人格驱动的冲突行为方面的有效性，通过评估框架和数据集方法，揭示LLMs在纠纷解决中与人类行为的显著差异。
摘要：
大型语言模型（LLMs）正在越来越多地被用于模拟人类行为，例如在法律调解、谈判和纠纷解决等社交场合中。然而，这些模拟是否能再现人类观察到的人格-行为模式仍不清楚。例如，人类的人格塑造了个体在社交互动中的行为方式，包括在情绪激烈互动中的战略选择和行为。这引发了问题：当LLMs被提示具有人格特质时，能否再现由人格驱动的人类冲突行为差异？为探讨这一问题，我们引入了一个评估框架，使人类-人类和LLM-LLM在纠纷解决对话中针对大五人格量表（BFI）人格特质进行直接比较。该框架提供了一组与战略行为和冲突结果相关的可解释指标。我们还贡献了一种新的数据集创建方法，用于生成具有匹配场景和人格特质的LLM纠纷解决对话，与人类对话相对应。最后，我们展示了使用我们的评估框架与三个当代闭源LLMs的使用，并显示了不同LLMs在冲突中人格表现与人类数据之间存在显著差异，挑战了假设即人格提示的代理可以作为在具有社会影响的应用中可靠的行为代理。我们的工作强调了在AI模拟应用于现实世界之前，需要心理学基础和验证。
英文摘要：
Large language models (LLMs) are increasingly used to simulate human behavior in social settings such as legal mediation, negotiation, and dispute resolution. However, it remains unclear whether these simulations reproduce the personality-behavior patterns observed in humans. Human personality, for instance, shapes how individuals navigate social interactions, including strategic choices and behaviors in emotionally charged interactions. This raises the question: Can LLMs, when prompted with personality traits, reproduce personality-driven differences in human conflict behavior? To explore this, we introduce an evaluation framework that enables direct comparison of human-human and LLM-LLM behaviors in dispute resolution dialogues with respect to Big Five Inventory (BFI) personality traits. This framework provides a set of interpretable metrics related to strategic behavior and conflict outcomes. We additionally contribute a novel dataset creation methodology for LLM dispute resolution dialogues with matched scenarios and personality traits with respect to human conversations. Finally, we demonstrate the use of our evaluation framework with three contemporary closed-source LLMs and show significant divergences in how personality manifests in conflict across different LLMs compared to human data, challenging the assumption that personality-prompted agents can serve as reliable behavioral proxies in socially impactful applications. Our work highlights the need for psychological grounding and validation in AI simulations before real-world use.
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。
