测试大模型能否在对话中保持稳定人格，发现表现差异大。
Can LLM Agents Maintain a Persona in Discourse?
- 用OCEAN五维人格设定双模型对话，模拟个性交互
- 多评估员判断显示人格一致性不足，模型间差异显著
- 适合关注对话连贯性与角色一致性的研究者参考
大型语言模型（LLMs）广泛应用于教育、法律、医疗等领域的对话系统。然而，它们常出现上下文漂移现象，导致人格特征不连贯且难以解释。现有研究缺乏对人格特质的全面分析，尤其在二人对话场景中。本文从双视角出发：首先让两个对话代理根据OCEAN框架（开放性、尽责性、外向性、宜人性、神经质）设定高/低人格值展开话题讨论；随后通过多个评判代理推断原始人格设定，评估预测一致性、模型间共识度及与设定人格的匹配程度。结果表明，尽管可引导模型生成人格化对话，但其维持人格的一致性因模型组合和对话环境而异，凸显了实现稳定、可解释的人格对齐交互的挑战。
原文摘要 · Abstract (English)
Large Language Models (LLMs) are widely used as conversational agents, exploiting their capabilities in various sectors such as education, law, medicine, and more. However, LLMs are often subjected to context-shifting behaviour, resulting in a lack of consistent and interpretable personality-aligned interactions. Adherence to psychological traits lacks comprehensive analysis, especially in the case of dyadic (pairwise) conversations. We examine this challenge from two viewpoints, initially using two conversation agents to generate a discourse on a certain topic with an assigned personality from the OCEAN framework (Openness, Conscientiousness, Extraversion, Agreeableness, and Neuroticism) as High/Low for each trait. This is followed by using multiple judge agents to infer the original traits assigned to explore prediction consistency, inter-model agreement, and alignment with the assigned personality. Our findings indicate that while LLMs can be guided toward personality-driven dialogue, their ability to maintain personality traits varies significantly depending on the combination of models and discourse settings. These inconsistencies emphasise the challenges in achieving stable and interpretable personality-aligned interactions in LLMs.
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