Nature Machine Intelligence· 2025Q1
A psychometric framework for evaluating and shaping personality traits in large language models
- 297citations
- Q1SCImago
- 2025year
Short summary
Researchers developed a framework to measure and modify personality traits in LLMs, finding that larger, instruction-tuned models exhibit stronger, more reliable synthetic personalities that can be shaped to mimic specific profiles.
AI-generated from the title and abstract; the full text is not read.
Key points
- Developed a comprehensive method for administering and validating personality tests on LLMs.
- Found personality measurements in LLM outputs to be reliable and valid under specific prompting.
- Demonstrated stronger evidence of synthetic LLM personality in larger, instruction fine-tuned models.
- Showed that LLM personality can be shaped along desired dimensions to mimic specific profiles.
AI-generated from the title and abstract; the full text is not read.
Abstract
Abstract The advent of large language models (LLMs) has revolutionized natural language processing, enabling the generation of coherent and contextually relevant text. As LLMs increasingly power conversational agents, the synthetic personality 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 comprehensive method 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, 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 personality profiles. We discuss application and ethical implications of the measurement and shaping method, in particular regarding responsible use of LLMs.
The authors' abstract, as published at the source. Nature Machine Intelligence, 2025 · DOI ↗
Continue with a free account
Ask the paper: 3 free questions a day about this paper; save it, get its citation, new summaries every day for your field. Takeaways are Premium.
Continue free on the webSign in with Google or Apple; no card needed. You come back to this paper.
On your phone:
Field: Artificial Intelligence Safety Research
Safety ResearchSocial SciencesArtificial Intelligence