# Practical PCG Through Large Language Models

arXiv: https://arxiv.org/abs/2305.18243 · HTML: https://mlsanity.com/paper/2305.18243/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 17 references that are in the map.
- First posted on arXiv 20 May 2023.

## Builds on (most cited first, 9 of 9)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Muse: Text-To-Image Generation via Masked Generative Transformers](https://mlsanity.com/paper/2301.00704/) (2023)
- [Reinforced Continual Learning](https://mlsanity.com/paper/1805.12369/) (2018)
- [Procedural Content Generation via Machine Learning (PCGML)](https://mlsanity.com/paper/1702.00539/) (2017)
- [Deep Learning for Procedural Content Generation](https://mlsanity.com/paper/2010.04548/) (2020)
- [Level Generation Through Large Language Models](https://mlsanity.com/paper/2302.05817/) (2023)
- [MarioGPT: Open-Ended Text2Level Generation through Large Language Models](https://mlsanity.com/paper/2302.05981/) (2023)
- [Bootstrapping Conditional GANs for Video Game Level Generation](https://mlsanity.com/paper/1910.01603/) (2019)
- [Augmentative Topology Agents For Open-Ended Learning](https://mlsanity.com/paper/2210.11442/) (2022)

## Built on by (most cited first, 21 of 21)

- [Large Language Models and Games: A Survey and Roadmap](https://mlsanity.com/paper/2402.18659/) (2024)
- [LLMatic: Neural Architecture Search via Large Language Models and Quality Diversity Optimization](https://mlsanity.com/paper/2306.01102/) (2023)
- [Game Generation via Large Language Models](https://mlsanity.com/paper/2404.08706/) (2024)
- [Large Language Models and Video Games: A Preliminary Scoping Review](https://mlsanity.com/paper/2403.02613/) (2024)
- [Player-Driven Emergence in LLM-Driven Game Narrative](https://mlsanity.com/paper/2404.17027/) (2024)
- [MultiGen: Level-Design for Editable Multiplayer Worlds in Diffusion Game Engines](https://mlsanity.com/paper/2603.06679/) (2026)
- [Unbounded: A Generative Infinite Game of Character Life Simulation](https://mlsanity.com/paper/2410.18975/) (2024)
- [GPT for Games: A Scoping Review (2020-2023)](https://mlsanity.com/paper/2404.17794/) (2024)
- [GPT for Games: An Updated Scoping Review (2020-2024)](https://mlsanity.com/paper/2411.00308/) (2024)
- [GameTraversalBenchmark: Evaluating Planning Abilities Of Large Language Models Through Traversing 2D Game Maps](https://mlsanity.com/paper/2410.07765/) (2024)
- [Grammar-based Game Description Generation using Large Language Models](https://mlsanity.com/paper/2407.17404/) (2024)
- [AnimeGamer: Infinite Anime Life Simulation with Next Game State Prediction](https://mlsanity.com/paper/2504.01014/) (2025)
- [3D Building Generation in Minecraft via Large Language Models](https://mlsanity.com/paper/2406.08751/) (2024)
- [Word2Minecraft: Generating 3D Game Levels through Large Language Models](https://mlsanity.com/paper/2503.16536/) (2025)
- [AI for Games in the Foundation Model Era](https://mlsanity.com/paper/2609.16679/) (2026)
- [Grammar and Gameplay-aligned RL for Game Description Generation with LLMs](https://mlsanity.com/paper/2503.15783/) (2025)
- [Symbolically Scaffolded Play: Designing Role-Sensitive Prompts for Generative NPC Dialogue](https://mlsanity.com/paper/2510.25820/) (2025)
- [Mortar: Evolving Mechanics for Automatic Game Design](https://mlsanity.com/paper/2601.00105/) (2025)
- [From World-Gen to Quest-Line: A Dependency-Driven Prompt Pipeline for Coherent RPG Generation](https://mlsanity.com/paper/2604.25482/) (2026)
- [Hybrid Voting-Based Task Assignment in Role-Playing Games](https://mlsanity.com/paper/2502.18690/) (2025)
- [Procedural Content Metageneration via Program Search and Continual Abstraction Discovery](https://mlsanity.com/paper/2608.17947/) (2026)

## Abstract (arXiv)

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.