  With the recent introduction of Assistants API, it is expected that
document-based language models will be actively used in various domains,
especially Role-playing. However, a key challenge lies in utilizing
protagonist's persona: Assistants API often fails to achieve with its search
because the information extraction part is different each time and it often
omits important information such as protagonist's backstory or relationships.
It is hard to maintain a consistent persona simply by using the persona
document as input to the Assistants API. To address the challenge of achieving
stable persona consistency, we propose CharacterGPT, a novel persona
reconstruction framework to alleviate the shortcomings of the Assistants API.
Our method involves Character Persona Training (CPT), an effective persona
rebuilding process that updates the character persona by extracting the
character's traits from given summary of the novel for each character as if the
story in a novel progresses. In our experiments, we ask each character to take
the Big Five Inventory personality test in various settings and analyze the
results. To assess whether it can think outside the box, we let each character
generate short novels. Extensive experiments and human evaluation demonstrate
that CharacterGPT presents new possibilities for role-playing agent research.
Code and results are available at: https://github.com/Jeiyoon/charactergpt
