通过动态优化提示词，让大模型更真实地表达个性。
Profile-LLM: Dynamic Profile Optimization for Realistic Personality Expression in LLMs
- 用大模型自身的人格知识迭代优化角色提示词
- 生成的提示词在人格表达上优于传统心理学描述
- 可控制优化过程来调节特定人格特质的强度
个性化大语言模型能显著提升人机交互的吸引力。现有研究多依赖心理学术语设计提示词以激发特定人格，但未对提示词进行优化以最大化人格表现。为此，我们提出PersonaPulse框架：利用大模型对人格特质的内在知识，迭代增强角色扮演提示词，并引入情境回应评估作为评分工具，确保评价更具现实性和上下文相关性。定量实验表明，PersonaPulse生成的提示词在人格表达上优于基于心理学研究设计的提示词。我们还通过大量实验探索了模型规模与人格建模的关系。此外，发现对某些人格特质，暂停优化过程可部分控制其表达程度。这些结果强调了提示词优化在塑造大模型人格表达中的关键作用，为未来自适应人机交互研究提供重要启示。
原文摘要 · Abstract (English)
Personalized Large Language Models (LLMs) have been shown to be an effective way to create more engaging and enjoyable user-AI interactions. While previous studies have explored using prompts to elicit specific personality traits in LLMs, they have not optimized these prompts to maximize personality expression. To address this limitation, we propose PersonaPulse: Dynamic Profile Optimization for Realistic Personality Expression in LLMs, a framework that leverages LLMs' inherent knowledge of personality traits to iteratively enhance role-play prompts while integrating a situational response benchmark as a scoring tool, ensuring a more realistic and contextually grounded evaluation to guide the optimization process. Quantitative evaluations demonstrate that the prompts generated by PersonaPulse outperform those of prior work, which were designed based on personality descriptions from psychological studies. Additionally, we explore the relationship between model size and personality modeling through extensive experiments. Finally, we find that, for certain personality traits, the extent of personality evocation can be partially controlled by pausing the optimization process. These findings underscore the importance of prompt optimization in shaping personality expression within LLMs, offering valuable insights for future research on adaptive AI interactions.
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