Word2Minecraft: Generating 3D Game Levels through Large Language Models
This addresses the problem of automated game level generation for game designers and developers, though it appears incremental as it applies existing LLMs to a new domain.
The researchers tackled the problem of generating playable Minecraft game levels from structured stories using large language models, with results showing GPT-4-Turbo outperforming GPT-4o-Mini in story coherence and objective enjoyment while achieving high map enjoyment.
We present Word2Minecraft, a system that leverages large language models to generate playable game levels in Minecraft based on structured stories. The system transforms narrative elements-such as protagonist goals, antagonist challenges, and environmental settings-into game levels with both spatial and gameplay constraints. We introduce a flexible framework that allows for the customization of story complexity, enabling dynamic level generation. The system employs a scaling algorithm to maintain spatial consistency while adapting key game elements. We evaluate Word2Minecraft using both metric-based and human-based methods. Our results show that GPT-4-Turbo outperforms GPT-4o-Mini in most areas, including story coherence and objective enjoyment, while the latter excels in aesthetic appeal. We also demonstrate the system' s ability to generate levels with high map enjoyment, offering a promising step forward in the intersection of story generation and game design. We open-source the code at https://github.com/JMZ-kk/Word2Minecraft/tree/word2mc_v0