CharacterGPT：用于角色扮演智能体的人格重建框架
CharacterGPT: A Persona Reconstruction Framework for Role-Playing Agents
AI总结：
CharacterGPT通过角色人格训练动态重建角色人格，解决角色扮演智能体中人格一致性问题，实验验证其有效性。
摘要：
近期推出的Assistants API凸显了大型语言模型（LLMs）在角色扮演智能体（RPA）中的潜力。然而，由于信息提取的变异性，维持一致的角色人格仍然是一个重大挑战，这种变异性经常遗漏关键元素，如背景故事或人际关系。为解决这一局限，我们引入了CharacterGPT，一个通过角色人格训练（CPT）动态重建角色人格的框架。该方法通过从章节式小说摘要中提取特质来逐步更新人格，反映叙事的进展。我们的框架通过大五人格评估和创造性任务进行评估，在这些任务中角色生成原创叙事，证明了CharacterGPT在保持人格一致性方面的有效性。代码和结果可在https://github.com/Jeiyoon/charactergpt获取。
英文摘要：
The recent introduction of the Assistants API highlights its potential for large language models (LLMs) in role-playing agents (RPA). However, maintaining consistent character personas remains a significant challenge due to variability in information extraction, which frequently omits critical elements such as backstory or interpersonal relationships. To address this limitation, we introduce CharacterGPT, a framework designed to dynamically reconstruct character personas through Character Persona Training (CPT). This approach incrementally updates personas by extracting traits from chapter-wise novel summaries, reflecting the progression of the narrative. Our framework is evaluated through Big Five personality evaluations and creative tasks, in which characters generate original narratives, demonstrating the efficacy of CharacterGPT in preserving persona consistency. The code and results are available at https://github.com/Jeiyoon/charactergpt
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。
