# Game Generation via Large Language Models

arXiv: https://arxiv.org/abs/2404.08706 · HTML: https://mlsanity.com/paper/2404.08706/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 24 references that are in the map.
- First posted on arXiv 11 Apr 2024.

## Builds on (most cited first, 14 of 14)

- [A Survey of Large Language Models](https://mlsanity.com/paper/2303.18223/) (2023)
- [Genie: Generative Interactive Environments](https://mlsanity.com/paper/2402.15391/) (2024)
- [Procedural Content Generation via Machine Learning (PCGML)](https://mlsanity.com/paper/1702.00539/) (2017)
- [Large Language Models and Games: A Survey and Roadmap](https://mlsanity.com/paper/2402.18659/) (2024)
- [Learning to Simulate Dynamic Environments with GameGAN](https://mlsanity.com/paper/2005.12126/) (2020)
- [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)
- [General Video Game AI: a Multi-Track Framework for Evaluating Agents, Games and Content Generation Algorithms](https://mlsanity.com/paper/1802.10363/) (2018)
- [Practical PCG Through Large Language Models](https://mlsanity.com/paper/2305.18243/) (2023)
- [General Video Game Rule Generation](https://mlsanity.com/paper/1906.05160/) (2019)
- [Games for Artificial Intelligence Research: A Review and Perspectives](https://mlsanity.com/paper/2304.13269/) (2023)
- [A Controllable Co-Creative Agent for Game System Design](https://mlsanity.com/paper/2308.02317/) (2023)
- [Active Learning for Classifying 2D Grid-Based Level Completability](https://mlsanity.com/paper/2309.04367/) (2023)

## Built on by (most cited first, 25 of 25)

- [Word2World: Generating Stories and Worlds through Large Language Models](https://mlsanity.com/paper/2405.06686/) (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: An Updated Scoping Review (2020-2024)](https://mlsanity.com/paper/2411.00308/) (2024)
- [GVGAI-LLM: Evaluating Large Language Model Agents with Infinite Games](https://mlsanity.com/paper/2508.08501/) (2025)
- [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)
- [MVPainter: Accurate and Detailed 3D Texture Generation via Multi-View Diffusion with Geometric Control](https://mlsanity.com/paper/2505.12635/) (2025)
- [From Code to Play: Benchmarking Program Search for Games Using Large Language Models](https://mlsanity.com/paper/2412.04057/) (2024)
- [Connecting the Dots: Evaluating Abstract Reasoning Capabilities of LLMs Using the New York Times Connections Word Game](https://mlsanity.com/paper/2406.11012/) (2024)
- [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)
- [ScriptDoctor: Automatic Generation of PuzzleScript Games via Large Language Models and Tree Search](https://mlsanity.com/paper/2506.06524/) (2025)
- [Boardwalk: Towards a Framework for Creating Board Games with LLMs](https://mlsanity.com/paper/2508.16447/) (2025)
- [Narrative-to-Scene Generation: An LLM-Driven Pipeline for 2D Game Environments](https://mlsanity.com/paper/2509.04481/) (2025)
- [From Gameplay Traces to Game Mechanics: Causal Induction with Large Language Models](https://mlsanity.com/paper/2602.00190/) (2026)
- [Multi-Agent Game Generation and Evaluation via Audio-Visual Recordings](https://mlsanity.com/paper/2508.00632/) (2025)
- [From World-Gen to Quest-Line: A Dependency-Driven Prompt Pipeline for Coherent RPG Generation](https://mlsanity.com/paper/2604.25482/) (2026)
- [MAGIC: Transition-Aware Generation of Navigable Multi-Scene Game Worlds with Large Language Models](https://mlsanity.com/paper/2607.11594/) (2026)
- [GameASG-Bench: Benchmarking Autonomous Software Generation for Game Development](https://mlsanity.com/paper/2609.21293/) (2026)
- [RSIGame: Autonomous Agentic Game Development with Recursive Self-improvement](https://mlsanity.com/paper/2609.39045/) (2026)
- [Procedural Content Metageneration via Program Search and Continual Abstraction Discovery](https://mlsanity.com/paper/2608.17947/) (2026)
- [Programmable Cellular Automata](https://mlsanity.com/paper/2609.06102/) (2026)
- [World Agent: Can Language Models Keep a World Running?](https://mlsanity.com/paper/2609.32692/) (2026)
- [World Editing: Intervening on Executable Worlds at Increasing Depth](https://mlsanity.com/paper/2610.02331/) (2026)

## Abstract (arXiv)

Recently, the emergence of large language models (LLMs) has unlocked new opportunities for procedural content generation. However, recent attempts mainly focus on level generation for specific games with defined game rules such as Super Mario Bros. and Zelda. This paper investigates the game generation via LLMs. Based on video game description language, this paper proposes an LLM-based framework to generate game rules and levels simultaneously. Experiments demonstrate how the framework works with prompts considering different combinations of context. Our findings extend the current applications of LLMs and offer new insights for generating new games in the area of procedural content generation.