Fine-tuning LLaMA-3 on GPT-4-generated GDD-code pairs yields a model that writes Unity C# templates from game design documents, but the reported 4.8/5.0 advantage over baselines rests on a weak three-game, three-rater evaluation.
GPT for Games: A Scoping Review (2020-2023)
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
abstract
This paper introduces a scoping review of 55 articles to explore GPT's potential for games, offering researchers a comprehensive understanding of the current applications and identifying both emerging trends and unexplored areas. We identify five key applications of GPT in current game research: procedural content generation, mixed-initiative game design, mixed-initiative gameplay, playing games, and game user research. Drawing from insights in each of these application areas, we propose directions for future research in each one. This review aims to lay the groundwork by illustrating the state of the art for innovative GPT applications in games, promising to enrich game development and enhance player experiences with cutting-edge AI innovations.
citation-role summary
background 1
citation-polarity summary
fields
cs.AI 1years
2025 1verdicts
REJECT 1roles
background 1polarities
background 1representative citing papers
citing papers explorer
-
Automated Unity Game Template Generation from GDDs via NLP and Multi-Modal LLMs
Fine-tuning LLaMA-3 on GPT-4-generated GDD-code pairs yields a model that writes Unity C# templates from game design documents, but the reported 4.8/5.0 advantage over baselines rests on a weak three-game, three-rater evaluation.