ArticleCommunications psychology2025
AI can outperform humans in predicting correlations between personality items.
Article in Communications psychology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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Who cites it
4 citing papers in PubMed.
- Inferring Personality From Social Media Activity Using Large Language Models: Cross-Model Agreement, Temporal Stability, and Convergent Validity With Self-Reports.Journal of personality · 2026Article
- Metabolite-mediated plant immunity: From traditional defenders to artificial elicitors.Journal of integrative plant biology · 2026Review
- Observer-Independent Assessment of Content Overlap in Mental Health Questionnaires: Large Language Model-Based Study.JMIR AI · 2025Article
- AI-Powered Plant Science: Transforming Forestry Monitoring, Disease Prediction, and Climate Adaptation.Plants (Basel, Switzerland) · 2025Review
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We assess the abilities of both specialized deep neural networks, such as PersonalityMap, and general LLMs, including GPT-4o and Claude 3 Opus, in understanding human personality by predicting correlations between personality questionnaire items. All AI models outperform the vast majority of laypeople and academic experts. However, we can improve the accuracy of individual correlation predictions by taking the median prediction per group to produce a "wisdom of the crowds" estimate. Thus, we also compare the median predictions from laypeople, academic experts, GPT-4o/Claude 3 Opus, and PersonalityMap. Based on medians, PersonalityMap and academic experts surpass both LLMs and laypeople on most measures. These results suggest that while advanced LLMs make superior predictions compared to most individual humans, specialized models like PersonalityMap can match even expert group-level performance in domain-specific tasks. This underscores the capabilities of large language models while emphasizing the continued relevance of specialized systems as well as human experts for personality research.
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Registered trials
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