GENEVA: GENErating and Visualizing branching narratives using LLMs

• URL: http://arxiv.org/abs/2311.09213v3
• Date: Wed, 5 Jun 2024 20:59:27 GMT
• Title: GENEVA: GENErating and Visualizing branching narratives using LLMs
• Authors: Jorge Leandro, Sudha Rao, Michael Xu, Weijia Xu, Nebosja Jojic, Chris Brockett, Bill Dolan,
• Abstract summary: textbfGENEVA, a prototype tool, generates a rich narrative graph with branching and reconverging storylines. textbfGENEVA has the potential to assist in game development, simulations, and other applications with game-like properties.
• Score: 15.43734266732214
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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