Prompted LLMs as Chatbot Modules for Long Open-domain Conversation
Gibbeum Lee, Volker Hartmann, Jongho Park, Dimitris Papailiopoulos, Kangwook Lee
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.- Anthology ID:
- 2023.findings-acl.277
- Volume:
- Findings of the Association for Computational Linguistics: ACL 2023
- Month:
- July
- Year:
- 2023
- Address:
- Toronto, Canada
- Editors:
- Anna Rogers, Jordan Boyd-Graber, Naoaki Okazaki
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 4536–4554
- Language:
- URL:
- https://aclanthology.org/2023.findings-acl.277
- DOI:
- 10.18653/v1/2023.findings-acl.277
- Cite (ACL):
- Gibbeum Lee, Volker Hartmann, Jongho Park, Dimitris Papailiopoulos, and Kangwook Lee. 2023. Prompted LLMs as Chatbot Modules for Long Open-domain Conversation. In Findings of the Association for Computational Linguistics: ACL 2023, pages 4536–4554, Toronto, Canada. Association for Computational Linguistics.
- Cite (Informal):
- Prompted LLMs as Chatbot Modules for Long Open-domain Conversation (Lee et al., Findings 2023)
- PDF:
- https://preview.aclanthology.org/naacl-24-ws-corrections/2023.findings-acl.277.pdf