Data Augmentation for Modeling Human Personality: The Dexter Machine
This addresses the challenge of expensive or unavailable labeled data for AI applications like artificial psychotherapists and persona bots, though it appears incremental as it builds on existing generative models.
The paper tackled the problem of limited labeled data for computational personality analysis, especially for rare personality types, by developing a text-based data augmentation approach called PEDANT that uses GPT and domain expertise, resulting in generated data that shows quality across three datasets.
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.