Title:"Mirror" Large Language Model Evaluations of Depression are Criterion Contaminated

Abstract:Large Language Model (LLM) studies that use language responses elicited from depression assessments to predict scores on those same assessments often report near-perfect prediction of depression. We refer to these as "Mirror" evaluations and demonstrate an applied case of criterion contamination. N = 110 participants completed both structured diagnostic depression interviews (Mirror condition) and life history interviews ("Non-Mirror" condition). LLMs were prompted to predict depression scores in each condition. As expected, Mirror evaluations were near-perfect. However, Non-Mirror evaluations also displayed prediction sizes considered outstanding in psychology. Further, both Mirror and Non-Mirror predictions correlated with Patient Health Questionnaire-9 scores at similar sizes, suggesting the Mirror condition's advantage collapses when predicting an independent depression measurement. Topic modeling revealed differing depression-related themes across interview types. Mirror evaluations are better considered as reliability evaluations than as validity evaluations. Incorporating Non-Mirror approaches in LLM depression assessment may support more valid and clinically-relevant applications. Keywords: large language models, psychological assessment, psychopathology, depression, reliability, validity, criterion contamination

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