Personality Traits in Large Language Models
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Cited by 39 in LLM agents and tool use, but placed by its own abstract, which didn’t fit.
Abstract (arXiv)
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 powerconversational agents used by the general public world-wide, the synthetic personality traits embedded in these models, by virtue of training on large amounts of human data, is becoming increasingly important. Since personality is a key factor determining the effectiveness of communication, we present a novel and comprehensive psychometrically valid and reliable 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: 1) personality measurements in the outputs of some LLMs under specific prompting configurations are reliable and valid; 2) evidence of reliability and validity of synthetic LLM personality is stronger for larger and instruction fine-tuned models; and 3) 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 AI.
Key facts. Cited by 169 papers in the map (data through 14 Oct 2026). Builds on 53 of its 183 references that are in the map. First posted on arXiv 1 Jul 2023.
Builds on
53 of 183 references.
References · 53
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Citing papers per year: 2023: 22; 2024: 51; 2025: 52; 2026: 44.
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- 2026Stabilising Generative Models of Attitude Change2 Apr 2026
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