GENEVA: GENErating and Visualizing branching narratives using LLMs
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2023
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| _version_ | 1866913379335012352 |
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| author | Leandro, Jorge Rao, Sudha Xu, Michael Xu, Weijia Jojic, Nebosja Brockett, Chris Dolan, Bill |
| author_facet | Leandro, Jorge Rao, Sudha Xu, Michael Xu, Weijia Jojic, Nebosja Brockett, Chris Dolan, Bill |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2311_09213 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | GENEVA: GENErating and Visualizing branching narratives using LLMs Leandro, Jorge Rao, Sudha Xu, Michael Xu, Weijia Jojic, Nebosja Brockett, Chris Dolan, Bill Computation and Language 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. |
| title | GENEVA: GENErating and Visualizing branching narratives using LLMs |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2311.09213 |