Customizable Procedural Content Generation with LLMs - Júnior, Marcelo; Adaniya, Mario; Nunes, Luiz (Anais do Simpósio Brasileiro de Jogos e Entretenimento Digital (SBGames), 2025)
Introduction: Procedural Content Generation (PCG) faces challenges in creating customizable levels with specific characteristics, such as difficulty patterns and unique layouts. Large Language Models (LLMs) offer a promising solution due to their natural language understanding and generative capabilities. Objective: This study proposes fine-tuning the DeepSeek-R1 LLM to generate customizable 2D game levels (similar to The Legend of Zelda), evaluating its feasibility for PCG. Methodology: Using a dataset with around 1000 playable levels of the Zelda-like game the model (DeepSeek-R1-DistillLlama-8B-unsloth-bnb-4bit) was fine-tuned with instructions specifying element quantities (e.g., enemies, blocks), and then evaluated using various metrics, including playability (ASTAR agent), novelty (Levenshtein distance), diversity (graph-based analysis), and accuracy (input adherence). Results: The model achieved very high results on novelty, diversity and playability, but falling a little on the accuracy tests. However it still shows impressive adherence for the inputs specifications, demonstrating LLMs’ potential for customizable PCG, outperforming earlier models like GPT-2/3.