给大模型设定性格会影响合作行为，亲和性最关键。
Effects of personality steering on cooperative behavior in Large Language Model agents
- 用五大性格特质框架调节大模型行为
- 亲和性越高，合作意愿越强，尤其在早期模型中
- 后期模型更会挑对象合作，防被利用
大型语言模型（LLMs）越来越多地被用作战略与社会互动中的自主代理。尽管已有研究显示为LLMs赋予人格特质可影响其行为，但在控制条件下人格引导如何影响合作仍不明确。本研究通过重复囚徒困境游戏，考察人格引导对LLM代理合作行为的影响。基于五大性格框架，我们首先使用大五人格量表测量GPT-3.5-turbo、GPT-4o和GPT-5三个模型的基础人格得分，再比较基线与人格引导条件下的行为差异，并进一步独立操纵每个性格维度至极端值。结果表明，亲和性是所有模型中促进合作的主导因素，其他特质影响有限。显式人格信息虽提升合作，但也增加被剥削的风险，尤其在早期模型中；而后期模型则表现出更选择性的合作行为。这些发现表明，人格引导是一种行为偏差机制，而非确定性控制手段。
原文摘要 · Abstract (English)
Large language models (LLMs) are increasingly used as autonomous agents in strategic and social interactions. Although recent studies suggest that assigning personality traits to LLMs can influence their behavior, how personality steering affects cooperation under controlled conditions remains unclear. In this study, we examine the effects of personality steering on cooperative behavior in LLM agents using repeated Prisoner's Dilemma games. Based on the Big Five framework, we first measure basic personality scores of three models, GPT-3.5-turbo, GPT-4o, and GPT-5, using the Big Five Inventory. We then compare behavior under baseline and personality-informed conditions, and further analyze the effects of independently manipulating each personality dimension to extreme values. Our results show that agreeableness is the dominant factor promoting cooperation across all models, while other personality traits have limited impact. Explicit personality information increases cooperation but can also raise vulnerability to exploitation, particularly in earlier-generation models. In contrast, later-generation models exhibit more selective cooperation. These findings indicate that personality steering acts as a behavioral bias rather than a deterministic control mechanism.
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