In this paper, we propose MPC (Modular Prompted Chatbot), a new approach for creating high-quality conversational agents without the need for fine-tuning. Our method utilizes pre-trained large language models (LLMs) as individual modules for long-term consistency and flexibility, by using techniques such as few-shot prompting, chain-of-thought (CoT), and external memory. Our human evaluation results show that MPC is on par with fine-tuned chatbot models in open-domain conversations, making it an effective solution for creating consistent and engaging chatbots.

翻译：本文提出了MPC（模块化提示型聊天机器人），一种无需微调即可构建高质量对话代理的新方法。该方法利用预训练大语言模型（LLMs）作为独立模块，通过少样本提示、思维链（CoT）及外部记忆等技术支持长期一致性与灵活性。人工评估结果显示，MPC在开放域对话中与微调式聊天机器人模型性能相当，为构建一致且富有吸引力的聊天机器人提供了有效解决方案。