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整的视频游戏关卡？游戏关卡具有复杂的功能约束和多维空间关系，这与LLM在训练过程中通常所见的数据类型截然不同。此外，游戏关卡数据集难以获取，这可能会对这些数据需求极大的模型构成挑战。我们研究了使用LLM为游戏《推箱子》生成关卡的情况，发现LLM确实具备此能力，且其性能随数据集规模的增大而显著提升。我们还对控制LLM关卡生成器进行了初步实验，并探讨了未来工作的潜力方向。