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）已被证明在其初始领域（自然语言处理）之外的诸多领域中是实用的工具。在本研究中，我们提供了如何利用LLMs为一款名为Metavoidal的开发中游戏生成2D游戏房间的实践指导。我们的技术通过人在环中微调来利用GPT-3的能力，从而在仅使用60个人工设计房间的稀疏数据条件下，针对一款具有大量局部和全局约束的非平凡游戏（就程序化内容生成（PCG）而言），能够创建出37%可玩的关卡。