Driving Generative Agents With Their Personality
Lawrence J. Klinkert Stephanie Buongiorno Corey Clark
This research explores the potential of Large Language Models (LLMs) to
utilize psychometric values, specifically personality information, within the
context of video game character development. Affective Computing (AC) systems
quantify a Non-Player character's (NPC) psyche, and an LLM can take advantage
of the system's information by using the values for prompt generation. The
research shows an LLM can consistently represent a given personality profile,
thereby enhancing the human-like characteristics of game characters.
Repurposing a human examination, the International Personality Item Pool (IPIP)
questionnaire, to evaluate an LLM shows that the model can accurately generate
content concerning the personality provided. Results show that the improvement
of LLM, such as the latest GPT-4 model, can consistently utilize and interpret
a personality to represent behavior.