TY - GEN
T1 - Level Generation Through Large Language Models
AU - Todd, Graham
AU - Earle, Sam
AU - Nasir, Muhammad Umair
AU - Green, Michael Cerny
AU - Togelius, Julian
N1 - Publisher Copyright:
© 2023 ACM.
PY - 2023/4/12
Y1 - 2023/4/12
N2 - Large Language Models (LLMs) are powerful tools, capable of leveraging their training on natural language to write stories, generate code, and answer questions. But can they generate functional video game levels? Game levels, with their complex functional constraints and spatial relationships in more than one dimension, are very different from the kinds of data an LLM typically sees during training. Datasets of game levels are also hard to come by, potentially taxing the abilities of these data-hungry models. We investigate the use of LLMs to generate levels for the game Sokoban, finding that LLMs are indeed capable of doing so, and that their performance scales dramatically with dataset size. We also perform preliminary experiments on controlling LLM level generators and discuss promising areas for future work.
AB - Large Language Models (LLMs) are powerful tools, capable of leveraging their training on natural language to write stories, generate code, and answer questions. But can they generate functional video game levels? Game levels, with their complex functional constraints and spatial relationships in more than one dimension, are very different from the kinds of data an LLM typically sees during training. Datasets of game levels are also hard to come by, potentially taxing the abilities of these data-hungry models. We investigate the use of LLMs to generate levels for the game Sokoban, finding that LLMs are indeed capable of doing so, and that their performance scales dramatically with dataset size. We also perform preliminary experiments on controlling LLM level generators and discuss promising areas for future work.
KW - language models
KW - procedural content generation
KW - sokoban
KW - transformers
UR - http://www.scopus.com/inward/record.url?scp=85153576891&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85153576891&partnerID=8YFLogxK
U2 - 10.1145/3582437.3587211
DO - 10.1145/3582437.3587211
M3 - Conference contribution
AN - SCOPUS:85153576891
T3 - ACM International Conference Proceeding Series
BT - Proceedings of the 18th International Conference on the Foundations of Digital Games, FDG 2023
A2 - Lopes, Phil
A2 - Luz, Filipe
A2 - Liapis, Antonios
A2 - Engstrom, Henrik
PB - Association for Computing Machinery
T2 - 18th International Conference on the Foundations of Digital Games, FDG 2023
Y2 - 11 April 2023 through 14 April 2023
ER -