Authors:
Ciprian Paduraru
1
;
Marina Cernat
1
and
Alin Stefanescu
1
;
2
Affiliations:
1
Department of Computer Science, University of Bucharest, Romania
;
2
Institute for Logic and Data Science, Romania
Keyword(s):
Large Language Models, Retrieval Augmented Generation, Teacher Model, Fine-Tuning, Video Games, Active Assistance, Simulation Applications.
Abstract:
This paper explores the application of state-of-the-art natural language processing (NLP) technologies to improve the user experience in games. Our motivation stems from the realization that a virtual assistant’s input during games or simulation applications can significantly assist the user in real-time problem solving, suggestion generation, and dynamic adjustments. We propose a novel framework that seamlessly integrates large-scale language models (LLMs) into game environments and enables intelligent assistants to take the form of physical 3D characters or virtual background entities within the player narrative. Our evaluation considers computational requirements, latency and quality of results using techniques such as synthetic dataset generation, fine-tuning, Retrieval Augmented Generation (RAG) and security mechanisms. Quantitative and qualitative evaluations, including real user feedback, confirm the effectiveness of our approach. The framework is implemented as an open-source
plugin for the Unreal Engine and has already been successfully used in a game demo. The presented methods can be extended to simulation applications and serious games in general.
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