Personality Traits in Large Language Models
Authors: name: Mustafa Safdari, name: Greg Serapio-García, name: Clément Crepy, name: Stephen Fitz, name: Peter Romero, name: Luning Sun, name: Marwa Abdulhai, name: Aleksandra Faust, name: Maja Matarić

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
synthesized personality embedded in these models by virtue of their training on
large amounts of human-generated data draws attention. Since personality is an
important factor determining the effectiveness of communication, we present a
comprehensive method for administering validated psychometric tests and
quantifying, analyzing, and shaping personality traits exhibited in text
generated from widely-used LLMs. We find that: 1) personality simulated in the
outputs of some LLMs (under specific prompting configurations) is reliable and
valid; 2) evidence of reliability and validity of LLM-simulated 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 also discuss potential applications and ethical
implications of our measurement and shaping framework, especially regarding
responsible use of LLMs.