Explaining GPT-4's Schema of Depression Using Machine Behavior Analysis

Use of large language models such as ChatGPT (GPT-4) for mental health support has grown rapidly, emerging as a promising route to assess and help people with mood disorders, like depression. However, we have a limited understanding of GPT-4's schema of mental disorders, that is, how it internally associates and interprets symptoms. In this work, we leveraged contemporary measurement theory to decode how GPT-4 interrelates depressive symptoms to inform both clinical utility and theoretical understanding. We found GPT-4's assessment of depression: (a) had high overall convergent validity (r = .71 with self-report on 955 samples, and r = .81 with experts judgments on 209 samples); (b) had moderately high internal consistency (symptom inter-correlates r = .23 to .78 ) that largely aligned with literature and self-report; except that GPT-4 (c) underemphasized suicidality's -- and overemphasized psychomotor's -- relationship with other symptoms, and (d) had symptom inference patterns that suggest nuanced hypotheses (e.g. sleep and fatigue are influenced by most other symptoms while feelings of worthlessness/guilt is mostly influenced by depressed mood).

Adaptive Language-based Mental Health Assessment with Item-Response Theory

Mental health issues widely vary across individuals - the manifestations of signs and symptoms can be fairly heterogeneous. Recently, language-based depression and anxiety assessments have shown promise for capturing this heterogeneous nature by evaluating a patient's own language, but such approaches require a large sample of words per person to be accurate. In this work, we introduce adaptive language-based assessment - the task of iteratively estimating an individual's psychological score based on limited language responses to questions that the model also decides to ask. To this end, we explore two statistical learning-based approaches for measurement/scoring: classical test theory (CTT) and item response theory (IRT). We find that using adaptive testing in general can significantly reduce the number of questions required to achieve high validity (r ~ 0.7) with standardized tests, bringing down from 11 total questions down to 3 for depression and 5 for anxiety. Given the combinatorial nature of the problem, we empirically evaluate multiple strategies for both the ordering and scoring objectives, introducing two new methods: a semi-supervised item response theory based method (ALIRT), and a supervised actor-critic based model. While both of the models achieve significant improvements over random and fixed orderings, we find ALIRT to be a scalable model that achieves the highest accuracy with lower numbers of questions (e.g. achieves Pearson r ~ 0.93 after only 3 questions versus asking all 11 questions). Overall, ALIRT allows prompting a reduced number of questions without compromising accuracy or overhead computational costs.

Transfer and Active Learning for Dissonance Detection: Addressing the Rare-Class Challenge

While transformer-based systems have enabled greater accuracies with fewer training examples, data acquisition obstacles still persist for rare-class tasks -- when the class label is very infrequent (e.g. < 5% of samples). Active learning has in general been proposed to alleviate such challenges, but choice of selection strategy, the criteria by which rare-class examples are chosen, has not been systematically evaluated. Further, transformers enable iterative transfer-learning approaches. We propose and investigate transfer- and active learning solutions to the rare class problem of dissonance detection through utilizing models trained on closely related tasks and the evaluation of acquisition strategies, including a proposed probability-of-rare-class (PRC) approach. We perform these experiments for a specific rare class problem: collecting language samples of cognitive dissonance from social media. We find that PRC is a simple and effective strategy to guide annotations and ultimately improve model accuracy while transfer-learning in a specific order can improve the cold-start performance of the learner but does not benefit iterations of active learning.