CrawLLM : theming games with large language models

Abstract

Video game players increasingly seek immersive and personalised experiences that resonate with their unique interests and personalities. This study explores the novel application of large language models (LLMs) in game re-theming, a process that adapts game assets to new settings and narratives. We applied this to an original game, CrawLLM, which combines dungeon crawling and card combat mechanics. The gameplay structure guided the construction of prompts for LLMs to generate new themes, stories, characters, and locations, which then directed the generation of corresponding visual assets. This approach enabled the game’s aesthetics to emerge from its underlying narrative. We hereby demonstrate a playable version of the game with 20 pre-generated, non-curated themes. This study paves the way for future research on automated game generation, where LLMs can orchestrate diverse content creation pipelines to construct entire games from high-level prompts, while maintaining cohesive user experiences.