Profile-LLM: Dynamic Profile Optimization for Realistic Personality Expression in LLMs

TL;DR

This paper introduces PersonaPulse, a framework that optimizes prompts for LLMs to more realistically and effectively express specific personality traits, improving user engagement and interaction quality.

Contribution

It presents a novel dynamic prompt optimization method leveraging LLMs' personality knowledge and situational evaluation, outperforming prior prompt design approaches.

Findings

Optimized prompts significantly outperform prior work in personality expression.

Model size influences the effectiveness of personality modeling.

Partial control over personality traits can be achieved by pausing optimization.

Abstract

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.