Word2World: Generating Stories and Worlds through Large Language Models

• URL: http://arxiv.org/abs/2405.06686v1
• Date: Mon, 6 May 2024 14:21:52 GMT
• Title: Word2World: Generating Stories and Worlds through Large Language Models
• Authors: Muhammad U. Nasir, Steven James, Julian Togelius,
• Abstract summary: Large Language Models (LLMs) have proven their worth across a diverse spectrum of disciplines. This work introduces Word2World, a system that enables LLMs to procedurally design playable games through stories.
• Score: 5.80330969550483
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Large Language Models (LLMs) have proven their worth across a diverse spectrum of disciplines. LLMs have shown great potential in Procedural Content Generation (PCG) as well, but directly generating a level through a pre-trained LLM is still challenging. This work introduces Word2World, a system that enables LLMs to procedurally design playable games through stories, without any task-specific fine-tuning. Word2World leverages the abilities of LLMs to create diverse content and extract information. Combining these abilities, LLMs can create a story for the game, design narrative, and place tiles in appropriate places to create coherent worlds and playable games. We test Word2World with different LLMs and perform a thorough ablation study to validate each step. We open-source the code at https://github.com/umair-nasir14/Word2World.

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