LLM-Based Learning for Enhancing Interaction Between Game Players and NPCs

Abstract

In today's games, traditional non-player character (NPC) behaviors and dialogues rely on preset scripts, making it difficult to dynamically respond to diverse player needs and limiting the immersion and interactive depth of the game. This study aims to explore the application of Large Language Models(LLM) in games to enhance the intelligent interaction of NPCs. In this study, the Llama model is fine-tuned to build a simulated game scene in Unity engine, and the fine-tuned model is embedded into an NPC system to verify its performance and ability to drive NPCs in real game interactions. In the experiments, the research team designed a series of typical game interaction scenarios, including item collection, task delivery, and complex semantic Q&A, through which the NPC's language comprehension and interaction capabilities were tested. The results demonstrate that LLM-driven NPCs enable game developers to build more flexible and intelligent interaction mechanisms. This approach opens new possibilities for intelligent NPC development.