L'IA peut-elle identifier les traits de personnalité dominants d'une personne à partir d'un échantillon d'écriture de 30 secondes avec une précision comparable à celle des psychologues formés ?

Les grands modèles de langage analysent les schémas linguistiques pour déduire les traits de personnalité Myers-Briggs ou Big Five. Des études montrent une forte corrélation avec les traits auto-déclarés et les évaluations d'observateurs. La précision s'améliore lorsque la longueur du texte augmente.

Background

Large language models analyze language patterns to infer Myers-Briggs or Big Five traits. Studies show strong correlation with self-reported traits and observer ratings. Accuracy improves when text length increases.

--- Current AI systems can infer broad personality traits such as the Big Five from brief text samples, and in some studies they match or exceed the accuracy of human experts when predicting traits like neuroticism, conscientiousness, or extraversion on samples as short as a few sentences. Techniques typically combine large language models fine-tuned on personality-annotated corpora with psycholinguistic features like LIWC categories, achieving around 0.3–0.4 correlation with ground-truth scales—comparable to inter-rater reliability between trained psychologists. However, these models rely on self-report questionnaires for training labels, which may not capture unconscious or context-sensitive traits, and performance drops when the writing sample contains atypical vocabulary, sarcasm, or cultural references not well represented in the training data. Ethical and privacy concerns also limit real-world deployment without explicit consent and robust safeguards.

— Enriched May 12, 2026 · Source: Matz et al., “Deep learning reveals predictive models of human language for personality assessment,” PNAS Nexus, 2023