Personality Traits in Large Language Models Greg Serapio-Garc ́ıa,1,2,3† Mustafa Safdari,1† Cle ́ment Crepy,4 Luning Sun,3Stephen Fitz,5 Peter Romero,3,5 Marwa Abdulhai,6 Aleksandra Faust,1‡ Maja Mataric ́1‡*

1Google DeepMind. 2Department of Psychology, University of Cambridge.
3The Psychometrics Centre, Cambridge Judge Business School, University of Cambridge. 4Google Research. 5Keio University. 6University of California, Berkeley. †Contributed equally. ‡Jointly supervised.

21 September 2023

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 world-wide, 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 human personality profiles. We discuss application and ethical implications of the measurement and shaping method, in particular regarding responsible AI.