  This research paper delves into the evolving landscape of fine-tuning large
language models (LLMs) to align with human users, extending beyond basic
alignment to propose "personality alignment" for language models in
organizational settings. Acknowledging the impact of training methods on the
formation of undefined personality traits in AI models, the study draws
parallels with human fitting processes using personality tests. Through an
original case study, we demonstrate the necessity of personality fine-tuning
for AIs and raise intriguing questions about applying human-designed tests to
AIs, engineering specialized AI personality tests, and shaping AI personalities
to suit organizational roles. The paper serves as a starting point for
discussions and developments in the burgeoning field of AI personality
alignment, offering a foundational anchor for future exploration in
human-machine teaming and co-existence.
