We highlight two significant issues leading to the passivity of current merchant non-player characters (NPCs): pricing and communication. While immersive interactions have been a focus, negotiations between merchant NPCs and players on item prices have not received sufficient attention. First, we define passive pricing as the limited ability of merchants to modify predefined item prices. Second, passive communication means that merchants can only interact with players in a scripted manner. To tackle these issues and create an active merchant NPC, we propose a merchant framework based on large language models (LLMs), called MART, which consists of an appraiser module and a negotiator module. We conducted two experiments to guide game developers in selecting appropriate implementations by comparing different training methods and LLM sizes. Our findings indicate that finetuning methods, such as supervised finetuning (SFT) and knowledge distillation (KD), are effective in using smaller LLMs to implement active merchant NPCs. Additionally, we found three irregular cases arising from the responses of LLMs. We expect our findings to guide developers in using LLMs for developing active merchant NPCs.

翻译：我们指出导致当前商人非玩家角色（NPC）被动性的两个核心问题：定价机制与交互方式。尽管沉浸式交互一直是研究重点，但商人NPC与玩家之间关于物品价格的协商尚未得到充分关注。首先，我们将被动定价定义为商人修改预设物品价格的能力受限；其次，被动交互指商人仅能通过预设脚本与玩家互动。为解决这些问题并创建主动型商人NPC，我们提出基于大型语言模型（LLM）的商人框架MART，该框架包含评估模块与协商模块。我们通过比较不同训练方法和LLM规模进行了两项实验，以指导游戏开发者选择合适的实施方案。研究结果表明，监督微调（SFT）和知识蒸馏（KD）等微调方法能有效利用较小规模的LLM实现主动型商人NPC。此外，我们发现了LLM响应中产生的三类异常案例。我们期望本研究能为开发者利用LLM开发主动型商人NPC提供实践指导。