Word2World: Generating Stories and Worlds through Large Language Models
<|reference_start|>Word2World: Generating Stories and Worlds through Large Language Models: 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.<|reference_end|>
@article{nasir2024word2world:,
    title={Word2World: Generating Stories and Worlds through Large Language Models},
    author={Muhammad U. Nasir, Steven James and Julian Togelius},
    journal={arXiv preprint arXiv:2405.06686},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.06686},
    primaryClass={cs.CL cs.AI}
}