@inproceedings{lee-etal-2023-prompted,
    title = "Prompted {LLM}s as Chatbot Modules for Long Open-domain Conversation",
    author = "Lee, Gibbeum  and
      Hartmann, Volker  and
      Park, Jongho  and
      Papailiopoulos, Dimitris  and
      Lee, Kangwook",
    editor = "Rogers, Anna  and
      Boyd-Graber, Jordan  and
      Okazaki, Naoaki",
    booktitle = "Findings of the Association for Computational Linguistics: ACL 2023",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.findings-acl.277",
    doi = "10.18653/v1/2023.findings-acl.277",
    pages = "4536--4554",
    abstract = "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.",
}

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.
Lee, Gibbeum Hartmann, Volker Park, Jongho Papailiopoulos, Dimitris Lee, Kangwook
Prompted LLMs as Chatbot Modules for Long Open-domain Conversation