This paper introduces an innovative task focused on editing the personality traits of Large Language Models (LLMs). This task seeks to adjust the models' responses to opinion-related questions on specified topics since an individual's personality often manifests in the form of their expressed opinions, thereby showcasing different personality traits. Specifically, we construct a new benchmark dataset PersonalityEdit to address this task. Drawing on the theory in Social Psychology, we isolate three representative traits, namely Neuroticism, Extraversion, and Agreeableness, as the foundation for our benchmark. We then gather data using GPT-4, generating responses that not only align with a specified topic but also embody the targeted personality trait. We conduct comprehensive experiments involving various baselines and discuss the representation of personality behavior in LLMs. Our intriguing findings uncover potential challenges of the proposed task, illustrating several remaining issues. We anticipate that our work can provide the NLP community with insights. Code and datasets will be released at https://github.com/zjunlp/EasyEdit.

翻译：本文提出了一项创新任务，旨在编辑大语言模型（LLMs）的个性特征。该任务试图调整模型对特定主题下意见相关问题的回应，因为个体的人格常通过其表达的观点得以体现，进而展现不同的个性特征。具体而言，我们构建了一个新的基准数据集PersonalityEdit以应对该任务。借鉴社会心理学理论，我们提炼出三种代表性特质——神经质、外倾性和宜人性——作为基准的基础。随后，我们利用GPT-4收集数据，生成既符合指定主题又体现目标个性特质的回应。我们开展了涵盖多种基准方法的全面实验，并讨论了大语言模型中个性行为的表现方式。有趣的研究发现揭示了该任务面临的潜在挑战，并阐明了若干待解决问题。我们期望这项工作能为自然语言处理学界提供启示。代码与数据集将发布于https://github.com/zjunlp/EasyEdit。