Myers-Briggs Personality Classification and Personality-Specific Language Generation Using Pre-trained Language Models
This addresses personality classification and generation for psychology and empathetic AI systems, but is incremental as it applies existing methods to a specific dataset.
The paper tackled predicting Myers-Briggs personality types from text using pre-trained language models, achieving 0.47 accuracy for all 4 types and 0.86 for at least 2 types, and explored using fine-tuned BERT for personality-specific language generation.
The Myers-Briggs Type Indicator (MBTI) is a popular personality metric that uses four dichotomies as indicators of personality traits. This paper examines the use of pre-trained language models to predict MBTI personality types based on scraped labeled texts. The proposed model reaches an accuracy of $0.47$ for correctly predicting all 4 types and $0.86$ for correctly predicting at least 2 types. Furthermore, we investigate the possible uses of a fine-tuned BERT model for personality-specific language generation. This is a task essential for both modern psychology and for intelligent empathetic systems.