Application of Large Language Models in Games

Junyou Chen; Ziyi Wang; Wenyi Zhang

doi:10.54254/2755-2721/2025.LD28270

Applied and Computational EngineeringOpen access

Application of Large Language Models in Games

Research Article

Open Access

Application of Large Language Models in Games

Junyou Chen ¹, Ziyi Wang ^2* Wenyi Zhang ³

¹ Zhong Guo High School, Shnaghai, China

² College of Cultural Industry Management, Hebei Institute of Communications, Hebei Province, China

³ Singapore Institute of Management Global Education, Singapore, Singapore

^*Corresponding author: wzhang036@mymail.sim.edu.sg

Published on 22 October 2025

ACE Vol.196

ISSN (Print): 2755-273X

ISSN (Online): 2755-2721

ISBN (Print): 978-1-80590-451-9

ISBN (Online): 978-1-80590-452-6

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Abstract

This paper explores the application of large language models (LLMs) in the gaming field. It begins by elaborating on the research background and significance, and reviews the current status of their application and research in the gaming domain. Subsequently, it introduces the characteristics, working principles, and development history of large language models. It then focuses on analyzing the application of large language models in games, taking LLM-controlled non-player characters (NPCs) and dynamic plot generation as examples to dissect their application methods and advantages in games. Finally, it discusses the challenges faced by large language models in gaming applications, such as high resource consumption and unstable interaction with players; at the same time, it looks forward to their impact on the development of the gaming field in the future, believing that with the continuous advancement of technology, they will promote the intelligent transformation of the gaming industry. This will play an important role in the future of the game industry.

Keywords:

Large Language Model, Natural Language Interaction, Gaming Application, Game Agent, Dynamic Content Generation.

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References

[1]. Mann, B., et al. (2020). Language models are few-shot learners. arXiv preprint arXiv: 2005.14165 1.3: 3.

[2]. Park, J. S., et al. (2023). "Generative agents: Interactive simulacra of human behavior. Proceedings of the 36th annual acm symposium on user interface software and technology.

[3]. Akoury, N., Qian, Y., and Mohit, I. (2023). A framework for exploring player perceptions of llm-generated dialogue in commercial video games. Findings of the Association for Computational Linguistics: EMNLP 2023.

[4]. Schick, T., et al. (2023). Toolformer: Language models can teach themselves to use tools. Advances in Neural Information Processing Systems 36: 68539-68551.

[5]. Weidinger, L., et al. (2021). Ethical and social risks of harm from language models. arXiv preprint arXiv: 2112.04359.

[6]. Wang, Z., Masri, Y., Malarvizhi, S. A., et al. (2025). Optimizing context-based location extraction by tuning open-source LLMs with RAG. International Journal of Digital Earth, 18(1).

[7]. Aman, S. S., Kone. T., N’guessan. G. B., et al. (2025). Learning to represent causality in recommender systems driven by large language models (LLMs). Discover Applied Sciences, 7(9): 960-960.

[8]. Dennstädt, F., Windisch, P., Filchenko, I., et al. (2025). Consensus Finding Among LLMs to Retrieve Information About Oncological Trials. Studies in health technology and informatics, 329239-243.

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[10]. Wu, G., Zheng, L., Xie, H., et al. (2025). Large Language Model Empowered Privacy-Protected Framework for PHI Annotation in Clinical Notes. Studies in health technology and informatics, 329876-880.

References

[1]. Mann, B., et al. (2020). Language models are few-shot learners. arXiv preprint arXiv: 2005.14165 1.3: 3.

[2]. Park, J. S., et al. (2023). "Generative agents: Interactive simulacra of human behavior. Proceedings of the 36th annual acm symposium on user interface software and technology.

[4]. Schick, T., et al. (2023). Toolformer: Language models can teach themselves to use tools. Advances in Neural Information Processing Systems 36: 68539-68551.

[5]. Weidinger, L., et al. (2021). Ethical and social risks of harm from language models. arXiv preprint arXiv: 2112.04359.

[6]. Wang, Z., Masri, Y., Malarvizhi, S. A., et al. (2025). Optimizing context-based location extraction by tuning open-source LLMs with RAG. International Journal of Digital Earth, 18(1).

[7]. Aman, S. S., Kone. T., N’guessan. G. B., et al. (2025). Learning to represent causality in recommender systems driven by large language models (LLMs). Discover Applied Sciences, 7(9): 960-960.

Cite this article

Chen,J.;Wang,Z.;Zhang,W. (2025). Application of Large Language Models in Games. Applied and Computational Engineering,196,74-80.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

About volume

Volume title: Proceedings of CONF-MLA 2025 Symposium: Intelligent Systems and Automation: AI Models, IoT, and Robotic Algorithms

ISBN: 978-1-80590-451-9(Print) / 978-1-80590-452-6(Online)

Editor: Hisham AbouGrad

Conference website: https://www.confmla.org/london.html

Conference date: 12 November 2025

Series: Applied and Computational Engineering

Volume number: Vol.196

ISSN: 2755-2721(Print) / 2755-273X(Online)

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