Abstract
Task-oriented dialogue systems need to generate appropriate responses to help fulfill users’ requests. This paper explores different strategies, namely prompting, retrieval, and fine-tuning, for task-oriented dialogue generation. Through a systematic evaluation, we aim to provide valuable insights and guidelines for researchers and practitioners working on developing efficient and effective dialogue systems for real-world applications. Evaluation is performed on the MultiWOZ and Taskmaster-2 datasets, and we test various versions of FLAN-T5, GPT-3.5, and GPT-4 models. Costs associated with running these models are analyzed, and dialogue evaluation is briefly discussed. Our findings suggest that when testing data differs from the training data, fine-tuning may decrease performance, favoring a combination of a more general language model and a prompting mechanism based on retrieved examples.- Anthology ID:
- 2023.sigdial-1.37
- Volume:
- Proceedings of the 24th Annual Meeting of the Special Interest Group on Discourse and Dialogue
- Month:
- September
- Year:
- 2023
- Address:
- Prague, Czechia
- Editors:
- Svetlana Stoyanchev, Shafiq Joty, David Schlangen, Ondrej Dusek, Casey Kennington, Malihe Alikhani
- Venue:
- SIGDIAL
- SIG:
- SIGDIAL
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 400–412
- Language:
- URL:
- https://aclanthology.org/2023.sigdial-1.37
- DOI:
- 10.18653/v1/2023.sigdial-1.37
- Cite (ACL):
- Gonçalo Raposo, Luisa Coheur, and Bruno Martins. 2023. Prompting, Retrieval, Training: An exploration of different approaches for task-oriented dialogue generation. In Proceedings of the 24th Annual Meeting of the Special Interest Group on Discourse and Dialogue, pages 400–412, Prague, Czechia. Association for Computational Linguistics.
- Cite (Informal):
- Prompting, Retrieval, Training: An exploration of different approaches for task-oriented dialogue generation (Raposo et al., SIGDIAL 2023)
- PDF:
- https://preview.aclanthology.org/fix-dup-bibkey/2023.sigdial-1.37.pdf