UMASS_BioNLP at MEDIQA-Chat 2023: Can LLMs generate high-quality synthetic note-oriented doctor-patient conversations?
Junda Wang, Zonghai Yao, Avijit Mitra, Samuel Osebe, Zhichao Yang, Hong Yu
Abstract
This paper presents UMASS_BioNLP team participation in the MEDIQA-Chat 2023 shared task for Task-A and Task-C. We focus especially on Task-C and propose a novel LLMs cooperation system named a doctor-patient loop to generate high-quality conversation data sets. The experiment results demonstrate that our approaches yield reasonable performance as evaluated by automatic metrics such as ROUGE, medical concept recall, BLEU, and Self-BLEU. Furthermore, we conducted a comparative analysis between our proposed method and ChatGPT and GPT-4. This analysis also investigates the potential of utilizing cooperation LLMs to generate high-quality datasets.- Anthology ID:
- 2023.clinicalnlp-1.49
- Volume:
- Proceedings of the 5th Clinical Natural Language Processing Workshop
- Month:
- July
- Year:
- 2023
- Address:
- Toronto, Canada
- Venue:
- ClinicalNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 460–471
- Language:
- URL:
- https://aclanthology.org/2023.clinicalnlp-1.49
- DOI:
- Cite (ACL):
- Junda Wang, Zonghai Yao, Avijit Mitra, Samuel Osebe, Zhichao Yang, and Hong Yu. 2023. UMASS_BioNLP at MEDIQA-Chat 2023: Can LLMs generate high-quality synthetic note-oriented doctor-patient conversations?. In Proceedings of the 5th Clinical Natural Language Processing Workshop, pages 460–471, Toronto, Canada. Association for Computational Linguistics.
- Cite (Informal):
- UMASS_BioNLP at MEDIQA-Chat 2023: Can LLMs generate high-quality synthetic note-oriented doctor-patient conversations? (Wang et al., ClinicalNLP 2023)
- PDF:
- https://preview.aclanthology.org/paclic-22-ingestion/2023.clinicalnlp-1.49.pdf