Machine Mindset：大型语言模型的 MBTI 探索
Machine Mindset: An MBTI Exploration of Large Language Models
AI总结：
本文提出“Machine Mindset”方法，通过两阶段微调和DPO将MBTI人格特质嵌入大型语言模型，确保人格稳定一致，并开源模型与数据，推动个性化AI发展。
摘要：
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，以解决个性化 AI 中人格一致性的挑战。我们的方法名为“Machine Mindset”，包含两阶段微调和直接偏好优化（DPO），将 MBTI 特质嵌入 LLMs。该方法确保模型内化这些特质，提供稳定且一致的人格画像。我们展示了模型在多个领域的有效性，表明模型表现与其各自的 MBTI 特质相一致。本文在人格数据集的开发和 LLMs 人格整合的新训练方法方面做出了重要贡献，增强了个性化 AI 应用的潜力。我们还在 https://github.com/PKU-YuanGroup/Machine-Mindset 开源了模型和部分数据。
英文摘要：
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}.
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。
