Large Language Models (LLMs) have proven to be useful tools in various domains outside of the field of their inception, which was natural language processing. In this study, we provide practical directions on how to use LLMs to generate 2D-game rooms for an under-development game, named Metavoidal. Our technique can harness the power of GPT-3 by Human-in-the-loop fine-tuning which allows our method to create 37% Playable-Novel levels from as scarce data as only 60 hand-designed rooms under a scenario of the non-trivial game, with respect to (Procedural Content Generation) PCG, that has a good amount of local and global constraints.

翻译：大型语言模型（LLMs）已在自然语言处理之外的多个领域展现出实用价值。本研究针对一款名为《Metavoidal》的开发中游戏，提供了利用LLMs生成二维游戏房间的实践指南。我们的技术通过"人在回路"微调机制，能够发挥GPT-3的生成能力，在仅使用60个手工设计房间的稀缺数据条件下，为具有显著局部与全局约束条件的非平凡游戏（从程序化内容生成视角而言）生成37%的可游玩关卡。