ArticleNature machine intelligence2025
A psychometric framework for evaluating and shaping personality traits in large language models.
Article in Nature machine intelligence, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.
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Who cites it
28 citing papers in PubMed.
- Personality Inference From Semistructured Interviews Using Large Language Models: Comparative Evaluation Study.Journal of medical Internet research · 2026Article
- Evaluating Large Language Model-Derived Outputs as Psychological Measures: A Five-Domain Psychometric Framework With Application to Daily Stress.Journal of medical Internet research · 2026Article
- Applying psychometrics to large language model simulated populations: recreating the HEXACO personality inventory experiment with generative agents.Royal Society open science · 2026Article
- The shrinking landscape of linguistic diversity in the age of large language models.Nature human behaviour · 2026Article
- A framework for cognitive personality priors in agentic AI: the role of personas in autonomous team science.BioData mining · 2026Article
- Persona-prompted LLM agents achieve modest but genuine prediction of human social media reactions.Scientific reports · 2026Article
- A validity-guided workflow for robust large language model research in psychology.Behavior research methods · 2026Article
- Unmasking conversational bias in AI multiagent systems.PloS one · 2026Article
- Metacognition of ChatGPT in confidence judgements.Frontiers in artificial intelligence · 2026Article
- Psychometric applications of generative artificial intelligence: Lifecycle, risks, and research agenda.PLOS mental health · 2026Review
- Private speech: similarities between a large language model and children.Frontiers in artificial intelligence · 2026Article
- Words matter: measuring and titrating the communicative character of AI.Frontiers in artificial intelligence · 2026Article
- Psychometric Evaluation of Large Language Model Embeddings for Personality Trait Prediction.Journal of medical Internet research · 2025Article
- Systematic Evaluation of Auto-Encoding and Large Language Model Representations for Capturing Author States and Traits.Findings of ACL. ACL · 2025Article
- LLM-based robot personality simulation and cognitive system.Scientific reports · 2025Article
- Probing Public Perceptions of Antidepressants on Social Media: Mixed Methods Study.JMIR formative research · 2025Article
- Article
- How Well Do Simulated Population Samples with GPT-4 Align with Real Ones? The Case of the Eysenck Personality Questionnaire Revised-Abbreviated Personality Test.Health data science · 2025Article
- Integrating large language models for intuitive robot navigation.Frontiers in robotics and AI · 2025Article
- Uncovering interactive effects of affective voice tone and personality diversity on dyadic creativity.Frontiers in psychology · 2025Article
Corrections and comments
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9 authors.
Funding
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Abstract
The advent of large language models (LLMs) has revolutionized natural language processing, enabling the generation of coherent and contextually relevant human-like text. As LLMs increasingly power conversational agents used by the general public worldwide, the synthetic personality traits embedded in these models by virtue of training on large amounts of human data are becoming increasingly important to evaluate. The style in which LLMs respond can mimic different human personality traits. Here, as these patterns can be a key factor determining the effectiveness of communication, we present a comprehensive psychometric methodology for administering and validating personality tests on widely used LLMs, as well as for shaping personality in the generated text of such LLMs. Applying this method to 18 LLMs, we found that: personality measurements in the outputs of some LLMs under specific prompting configurations are reliable and valid; evidence of reliability and validity of synthetic LLM personality is stronger for larger and instruction-fine-tuned models; and personality in LLM outputs can be shaped along desired dimensions to mimic specific human personality profiles. We discuss the application and ethical implications of the measurement and shaping method, in particular regarding responsible artificial intelligence.
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