  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.
