GPT-4 can anticipate aggregate human response patterns
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Human personality has traditionally been measured by asking people questions and comparing their answers across established psychological frameworks.
A new study published on iScience suggests GPT-4 can go further by estimating how people collectively respond to personality questions before answering begins.
Researchers Rotem Monsa, Aviv Zohar, and Shahar Arzy of the Hebrew University and Hadassah Medical School tested whether the model could infer patterns in human responses from language alone, rather than merely generating plausible questionnaire wording.
The team used GPT-4 to create personality questionnaires from two very different textual sources.
One questionnaire drew on the DSM-5, the standard clinical manual used worldwide by psychiatrists to diagnose personality disorders with precision.
The other version pulled statements from an astrology textbook that assigns particular personality traits to each of the twelve zodiac signs.
"We wanted to choose texts that describe human personality in very rich detail but also sit on opposite ends of a spectrum in terms of scientific grounding," said Rotem Monsa, lead author of the study.
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“The DSM-5 was refined through decades of clinical research and is the standard diagnostic manual in clinical psychiatry, known worldwide.”
Both questionnaires were then given to 600 participants alongside the Big Five Inventory, the most validated personality measurement tool available.
Before collecting those responses, GPT-4 estimated average scores for individual questions using a five-point Likert response scale.
This gave the researchers a way to compare predictions against subsequently collected evidence.
The model's estimates correlated strongly with actual participant averages, reaching 0.71 for DSM-derived questions and 0.85 for astrology-derived questions.
