This preprint evaluates whether GPT-4o and GPT-4o mini can infer Big Five traits from semi-structured interview transcripts for 102 participants. Each person answered five prompts about the previous day, a challenging experience, coping, an unpleasant event, and a positive event; responses were paired with BFI-10 self-reports and depressive-symptom information. The study compares two zero-shot routes: directly request five trait scores from 1 to 5, or first request all ten BFI-10 item scores and then calculate traits. The sample is highly imbalanced: 96 men and 6 women, ages 24–56, mean 36.63. Text is lowercased and stripped of stopwords and punctuation before model input. Direct results are weak. Pearson correlations with human BFI-10 scores range from −0.090 to 0.185; GPT-4o obtains 0.098 for Extraversion, 0.184 for Agreeableness, −0.058 for Conscientiousness, 0.025 for Neuroticism, and 0.142 for Openness. GPT-4o mini obtains 0.151, −0.063, −0.019, −0.090, and 0.185. Across the ten BFI items no correlation reaches 0.3; the joint range is −0.216 to 0.259. GPT-4o has the smaller absolute mean bias on four items and mini on six, but this is not individual error. The paper calls the signed mean difference between reference and prediction “accuracy.” Positive and negative errors can cancel, so a near-zero mean does not imply participant-level predictions are close. The paper reports no MAE, RMSE, concordance correlation, calibration, intervals, tests, or error dispersion. Reconstructing Table 3, the BFI-10 route reduces absolute mean bias in 7 of 10 model-trait combinations and worsens it in 3: GPT-4o/Extraversion moves from |−0.514| to |0.598|; mini/Conscientiousness from |0.157| to |0.475|; and mini/Openness from |0.162| to |−0.294|. The unweighted mean falls from 0.446 to 0.371, but it remains aggregate bias rather than MAE. Claims of a significant improvement and greater accuracy are therefore not established. The symptom analysis splits participants between none and at least one symptom. The method first attributes labels to SCID/SCID-5 interviews and later says that “a survey like PHQ-9” was used, without identifying the exact instrument or rule. Group sizes are not stated; table increments imply 79 without symptoms and 23 with symptoms, an arithmetic inference rather than a reported fact. Comparing mean bias within each group does not demonstrate sensitivity to a depression-related shift: reference distributions may differ, predicted and observed group deltas are not directly compared, and no uncertainty is reported. Mini’s “perfect alignment” on BFI-4 means only zero mean bias, not participant-level equality. Moreover, for BFI-9 in the symptom-present group Table 5 favors GPT-4o, 1.000 versus 1.087 absolute bias, while the prose says mini is better. Exact model snapshots, execution date, API parameters, seeds, repeat count, parser, exact BFI-10 scoring formula, code, and data are not reported. The faithful conclusion is that both models have very low correlations with the reference and systematic bias in this small, skewed sample. Imposing BFI-10 structure changes and sometimes reduces group-level bias, but it does not establish individual accuracy, reliability, clinical sensitivity, or an ability to assess personality or depression.
Research question
With what fidelity do GPT-4o and GPT-4o mini assign Big Five traits and BFI-10 items from five interview responses, whether calculating traits from predicted items improves the comparison with self-report, and whether the bias of the predictions differs between groups with and without depressive symptoms?