What if Red Can Talk? Dynamic Dialogue Generation Using Large Language Models

TL;DR

This paper explores using large language models, specifically GPT-4, combined with knowledge graphs, to generate dynamic, context-aware dialogues in RPGs, aiming to improve player immersion through more natural character interactions.

Contribution

It introduces a novel dialogue filler framework leveraging LLMs and knowledge graphs for dynamic NPC interactions in RPGs, tested in popular game environments.

Findings

GPT-4 can generate contextually appropriate dialogues with defined personalities.

The framework enhances player immersion through more natural character interactions.

Some personality traits, like subtlety or maturity, are less effectively modeled.

Abstract

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.