# GENEVA: GENErating and Visualizing branching narratives using LLMs

arXiv: https://arxiv.org/abs/2311.09213 · HTML: https://mlsanity.com/paper/2311.09213/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 35 references that are in the map.
- First posted on arXiv 15 Nov 2023.

## Builds on (most cited first, 1 of 1)

- [Story Designer: Towards a Mixed-Initiative Tool to Create Narrative Structures](https://mlsanity.com/paper/2210.09294/) (2022)

## Built on by (most cited first, 8 of 8)

- [GPT for Games: An Updated Scoping Review (2020-2024)](https://mlsanity.com/paper/2411.00308/) (2024)
- [Game Plot Design with an LLM-powered Assistant: An Empirical Study with Game Designers](https://mlsanity.com/paper/2411.02714/) (2024)
- [An LLM-Guided Tutoring System for Social Skills Training](https://mlsanity.com/paper/2501.09870/) (2025)
- [All Stories Are One Story: Emotional Arc Guided Procedural Game Level Generation](https://mlsanity.com/paper/2508.02132/) (2025)
- [Node-Based Editing for Multimodal Generation of Text, Audio, Image, and Video](https://mlsanity.com/paper/2511.03227/) (2025)
- [Symbolically Scaffolded Play: Designing Role-Sensitive Prompts for Generative NPC Dialogue](https://mlsanity.com/paper/2510.25820/) (2025)
- [JarvisHub: An Open Harness for Canvas-Native Multimodal Creative Agents](https://mlsanity.com/paper/2607.23588/) (2026)
- [Matching Game Preferences Through Dialogical Large Language Models: A Perspective](https://mlsanity.com/paper/2507.20000/) (2025)

## Abstract (arXiv)

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. 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.