Personality Emulation Utilizing Large Language Models

Abstract

Fake identities have proven to be an effective methodology for conducting privacy and cybersecurity research; however, existing models are limited in their ability to interact with and respond to received communications. To perform privacy research in more complex Internet domains, withstand enhanced scrutiny, and persist long-term, fake identities must be capable of automatically generating responses while maintaining consistent behavior and personality. This work proposes a method for assigning personality to fake identities using the widely accepted psychometric Big Five model. Leveraging this model, the potential application of large language models (LLMs) to generate email responses that emulate human personality traits is investigated to enhance fake identity capabilities for privacy research at scale.