A psychometric framework for evaluating and shaping personality traits in large language models.

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 meth

SUBMITTER: Serapio-Garcia G 

PROVIDER: S-EPMC12719228 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature