Profile-LLM: 动态配置优化以实现LLM中的真实人格表达
Profile-LLM: Dynamic Profile Optimization for Realistic Personality Expression in LLMs
AI总结：
Profile-LLM通过动态配置优化提升LLM中真实人格表达，通过迭代增强提示和情境评估工具，优化人格表现并探索模型大小与人格建模的关系。
摘要：
个性化的大语言模型（LLMs）已被证明是创建更吸引人和愉快的用户-人工智能互动的有效方法。尽管先前的研究已经探索了使用提示来激发LLM中的特定人格特质，但它们并未优化这些提示以最大化人格表达。为了解决这一限制，我们提出了PersonaPulse：动态配置优化以实现LLM中的真实人格表达，该框架利用LLM对人格特质的内在知识，通过迭代增强角色扮演提示，并整合情境响应基准作为评分工具，确保更真实和情境相关的评估以指导优化过程。定量评估表明，由PersonaPulse生成的提示优于先前工作生成的提示，这些提示是基于心理学研究中的人格描述设计的。此外，我们通过广泛的实验探讨了模型大小与人格建模之间的关系。最后，我们发现，对于某些人格特质，可以通过暂停优化过程来部分控制人格唤起的程度。这些发现强调了提示优化在塑造LLM中人格表达的重要性，为未来关于自适应人工智能互动的研究提供了有价值的见解。
英文摘要：
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.
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。
