Machine Mindset: An MBTI Exploration of Large Language Models
This work addresses the challenge of creating stable and consistent personality profiles in AI for personalized applications, representing an incremental advancement in the field.
The paper tackles the problem of personality consistency in personalized AI by integrating Myers-Briggs Type Indicator (MBTI) traits into large language models using a two-phase fine-tuning and Direct Preference Optimization method, resulting in models that demonstrate alignment between performance and MBTI traits.
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}.