Large language models has catalyzed the development of personalized dialogue systems, numerous role-playing conversational agents have emerged. While previous research predominantly focused on enhancing the model's capability to follow instructions by designing character profiles, neglecting the psychological factors that drive human conversations. In this paper, we propose Orca, a framework for data processing and training LLMs of custom characters by integrating personality traits. Orca comprises four stages: (1) Personality traits inferring, leverage LLMs to infer user's BigFive personality trait reports and scores. (2) Data Augment, simulate user's profile, background story, and psychological activities. (3) Dataset construction, personality-conditioned instruction prompting (PCIP) to stimulate LLMs. (4) Modeling and Training, personality-conditioned instruction tuning (PTIT and PSIT), using the generated data to enhance existing open-source LLMs. We introduce OrcaBench, the first benchmark for evaluating the quality of content generated by LLMs on social platforms across multiple scales. Our experiments demonstrate that our proposed model achieves superior performance on this benchmark, demonstrating its excellence and effectiveness in perceiving personality traits that significantly improve role-playing abilities. Our Code is available at https://github.com/Aipura/Orca.

翻译：大语言模型推动了个性化对话系统的发展，催生了众多角色扮演对话智能体。先前研究主要侧重于通过设计角色档案来增强模型遵循指令的能力，却忽视了驱动人类对话的心理因素。本文提出Orca，一个通过整合人格特质进行自定义角色数据处理与大语言模型训练的框架。Orca包含四个阶段：(1) 人格特质推断：利用大语言模型推断用户的五大人格特质报告与得分。(2) 数据增强：模拟用户的个人资料、背景故事与心理活动。(3) 数据集构建：采用人格条件指令提示（PCIP）激发大语言模型生成数据。(4) 建模与训练：通过人格条件指令微调（PTIT与PSIT），使用生成数据增强现有开源大语言模型。我们引入了OrcaBench，这是首个用于评估大语言模型在社交平台上生成内容质量的多尺度基准测试。实验表明，我们提出的模型在该基准上取得了优越性能，证明了其在感知人格特质方面具有卓越能力，能显著提升角色扮演水平。代码发布于 https://github.com/Aipura/Orca。