We present a novel approach for integrating Myers-Briggs Type Indicator (MBTI) personality traits into large language models (LLMs), addressing the challenges of personality consistency in personalized AI. Our method, "Machine Mindset," involves a two-phase fine-tuning and Direct Preference Optimization (DPO) to embed MBTI traits into LLMs. This approach ensures that models internalize these traits, offering a stable and consistent personality profile. We demonstrate the effectiveness of our models across various domains, showing alignment between model performance and their respective MBTI traits. The paper highlights significant contributions in the development of personality datasets and a new training methodology for personality integration in LLMs, enhancing the potential for personalized AI applications. We also open-sourced our model and part of the data at \url{https://github.com/PKU-YuanGroup/Machine-Mindset}.

翻译：我们提出了一种将迈尔斯-布里格斯类型指标（MBTI）人格特质整合到大型语言模型（LLMs）中的新方法，以应对个性化AI中人格一致性挑战。我们的方法“机器思维”采用两阶段微调与直接偏好优化（DPO），将MBTI特质嵌入LLMs。该方法确保模型内化这些特质，从而提供稳定且一致的人格特征。我们通过多个领域验证了模型的有效性，展示了模型性能与其相应MBTI特质之间的对齐。本文在人格数据集开发及LLMs人格整合新训练方法论方面作出了重要贡献，显著提升了个性化AI应用的潜力。此外，我们在\url{https://github.com/PKU-YuanGroup/Machine-Mindset}开源了模型及部分数据。