  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.
