Data Augmentation for Modeling Human Personality: The Dexter Machine

TL;DR

This paper introduces PEDANT, a novel text-based data augmentation method leveraging GPT and domain expertise to improve human personality modeling, especially for rare personality types, without relying on labeled data.

Contribution

The paper presents PEDANT, a new data augmentation approach that uses GPT and domain knowledge to generate training data for personality analysis without labeled datasets.

Findings

Supports quality of generated data across datasets

Effective for rare personality types

Enhances AI models for personality analysis

Abstract

Modeling human personality is important for several AI challenges, from the engineering of artificial psychotherapists to the design of persona bots. However, the field of computational personality analysis heavily relies on labeled data, which may be expensive, difficult or impossible to get. This problem is amplified when dealing with rare personality types or disorders (e.g., the anti-social psychopathic personality disorder). In this context, we developed a text-based data augmentation approach for human personality (PEDANT). PEDANT doesn't rely on the common type of labeled data but on the generative pre-trained model (GPT) combined with domain expertise. Testing the methodology on three different datasets, provides results that support the quality of the generated data.