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：我们提出了一种新颖的方法，将迈尔斯-布里格斯类型指标（MBTI）人格特质集成到大型语言模型（LLMs）中，解决了个性化人工智能中人格一致性的挑战。我们的方法“机器思维”涉及两阶段微调和直接偏好优化（DPO），以将MBTI特质嵌入LLMs。该方法确保模型内化这些特质，提供稳定且一致的人格特征。我们在多个领域展示了模型的有效性，表明模型性能与其相应MBTI特质之间的一致性。本文强调了在人格数据集开发以及用于LLMs人格集成的新训练方法方面的重要贡献，增强了个性化人工智能应用的潜力。我们还开源了模型及部分数据，网址为 \url{https://github.com/PKU-YuanGroup/Machine-Mindset}。