Large Language Models (LLMs) are powerful tools, capable of leveraging their training on natural language to write stories, generate code, and answer questions. But can they generate functional video game levels? Game levels, with their complex functional constraints and spatial relationships in more than one dimension, are very different from the kinds of data an LLM typically sees during training. Datasets of game levels are also hard to come by, potentially taxing the abilities of these data-hungry models. We investigate the use of LLMs to generate levels for the game Sokoban, finding that LLMs are indeed capable of doing so, and that their performance scales dramatically with dataset size. We also perform preliminary experiments on controlling LLM level generators and discuss promising areas for future work.

翻译：大型语言模型（LLMs）是功能强大的工具，能够利用其在自然语言上的训练来编写故事、生成代码和回答问题。但它们能否生成功能完备的视频游戏关卡呢？游戏关卡具有复杂的函数约束以及多维空间关系，这与LLM在训练过程中通常处理的数据类型截然不同。此外，游戏关卡数据集难以获取，这可能对这些依赖大量数据的模型构成挑战。我们研究了利用LLM为推箱子游戏生成关卡的方法，发现LLM确实能够完成这一任务，且其性能随数据集规模显著提升。我们还进行了关于控制LLM关卡生成器的初步实验，并讨论了未来具有前景的研究方向。