GENEVA: GENErating and Visualizing branching narratives using LLMs

TL;DR

GENEVA leverages GPT-4 to automatically generate and visualize complex branching narratives for dialogue-based RPGs, streamlining storytelling and aiding creative design processes.

Contribution

This work introduces GENEVA, a novel tool that uses large language models to generate and visualize branching storylines based on high-level descriptions and constraints.

Findings

Successfully generated branching narratives for well-known stories.

Demonstrated GENEVA's potential in game development and simulations.

Showcased the tool's ability to adapt to different constraints.

Abstract

Dialogue-based Role Playing Games (RPGs) require powerful storytelling. The narratives of these may take years to write and typically involve a large creative team. In this work, we demonstrate the potential of large generative text models to assist this process. \textbf{GENEVA}, a prototype tool, generates a rich narrative graph with branching and reconverging storylines that match a high-level narrative description and constraints provided by the designer. A large language model (LLM), GPT-4, is used to generate the branching narrative and to render it in a graph format in a two-step process. We illustrate the use of GENEVA in generating new branching narratives for four well-known stories under different contextual constraints. This tool has the potential to assist in game development, simulations, and other applications with game-like properties.