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English(EN) How Personas Can Influence Agents to Play Split or Steal

Persona塑造LLM Agent在策略游戏中的行为

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员调查了Persona提示如何影响大型语言模型Agent在“分还是偷”游戏中的策略行为。他们使用了四种开源模型（Ministral-3-3B、phi4:14b、Gemma3:12b和Gemma4:e4b）与一个由GPT-4.1 mini驱动的虚拟人类进行交互，发现相互“分”的结果占主导地位，约占回合数的74%。模型选择显著影响了Agent的行为，phi4和Ministral-3-3B始终表现出合作性，而Gemma模型则展现出更多样的策略。像“亲社会”和“有原则”这样的Persona特质与合作相关，而“分析型”Persona则更容易出现剥削行为。 AI

影响 展示了Persona提示如何影响LLM Agent在策略互动中的行为，为开发更可预测和可控的AI Agent提供了见解。

排序理由 学术论文，详细介绍了LLM Agent行为的实验结果。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CL 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

Persona塑造LLM Agent在策略游戏中的行为

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学术论文，详细介绍了LLM Agent行为的实验结果。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.CL TIER_1 English(EN) · Carlos Leon, Alexandre Rodrigues, Pedro Gamito, Thomas D. Parsons ·

    角色如何影响代理人玩“分而治之”或“偷窃”游戏

    arXiv:2607.05398v1 Announce Type: new Abstract: Personas are often employed to guide large language model agents, yet their effectiveness in shaping strategic behavior in social dilemma settings remains uncertain. To address this, we examined the impact of persona prompts in an i…

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