PsyPlay：注入人格的角色扮演对话智能体
PsyPlay: Personality-Infused Role-Playing Conversational Agents
AI总结：
本文提出PsyPlay框架，使多个LLM智能体在角色扮演对话中持续展现指定人格特质，在GPT-3.5上取得80.31%的成功率，并构建了含4745个实例的PsyPlay-Bench语料库以推动个性化角色扮演研究。
摘要：
当前基于大语言模型（LLMs）的角色扮演对话智能体（RPCAs）研究主要集中于模仿特定说话风格和利用角色背景，忽视了对更深层人格特质的刻画。在本研究中，我们为LLM智能体引入了注入人格的角色扮演，鼓励智能体在对话过程中准确呈现其指定的人格特质。随后，我们提出了PsyPlay，一个促进多个LLM智能体表达丰富人格的对话生成框架。具体而言，PsyPlay使智能体能够扮演具有不同人格特质的角色，并围绕特定主题展开讨论，在整个交互过程中持续展现其指定的人格特质。在生成的对话数据上的验证表明，PsyPlay能够准确呈现预期的人格特质，在GPT-3.5上取得了80.31%的总体成功率。值得注意的是，我们观察到与积极价值观对齐的LLM在扮演积极人格角色方面比扮演消极人格角色更为成功。此外，我们构建了一个用于注入人格角色扮演的对话语料库，名为PsyPlay-Bench。该语料库包含4745个使用PsyPlay正确呈现的对话实例，旨在进一步促进个性化角色扮演和对话人格检测方面的研究。
英文摘要：
The current research on Role-Playing Conversational Agents (RPCAs) with Large Language Models (LLMs) primarily focuses on imitating specific speaking styles and utilizing character backgrounds, neglecting the depiction of deeper personality traits.~In this study, we introduce personality-infused role-playing for LLM agents, which encourages agents to accurately portray their designated personality traits during dialogues. We then propose PsyPlay, a dialogue generation framework that facilitates the expression of rich personalities among multiple LLM agents. Specifically, PsyPlay enables agents to assume roles with distinct personality traits and engage in discussions centered around specific topics, consistently exhibiting their designated personality traits throughout the interactions. Validation on generated dialogue data demonstrates that PsyPlay can accurately portray the intended personality traits, achieving an overall success rate of 80.31% on GPT-3.5. Notably, we observe that LLMs aligned with positive values are more successful in portraying positive personality roles compared to negative ones. Moreover, we construct a dialogue corpus for personality-infused role-playing, called PsyPlay-Bench. The corpus, which consists of 4745 instances of correctly portrayed dialogues using PsyPlay, aims to further facilitate research in personalized role-playing and dialogue personality detection.
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。
