  Role-playing games (RPGs) provide players with a rich, interactive world to
explore. Dialogue serves as the primary means of communication between
developers and players, manifesting in various forms such as guides, NPC
interactions, and storytelling. While most games rely on written scripts to
define the main story and character personalities, player immersion can be
significantly enhanced through casual interactions between characters. With the
advent of large language models (LLMs), we introduce a dialogue filler
framework that utilizes LLMs enhanced by knowledge graphs to generate dynamic
and contextually appropriate character interactions. We test this framework
within the environments of Final Fantasy VII Remake and Pokemon, providing
qualitative and quantitative evidence that demonstrates GPT-4's capability to
act with defined personalities and generate dialogue. However, some flaws
remain, such as GPT-4 being overly positive or more subtle personalities, such
as maturity, tend to be of lower quality compared to more overt traits like
timidity. This study aims to assist developers in crafting more nuanced filler
dialogues, thereby enriching player immersion and enhancing the overall RPG
experience.
