Abstract
Question answering involves creating answers to questions. With the growth of large language models, the ability of question-answering systems has dramatically improved. However, there is a lack of Vietnamese abstractive question-answering datasets, especially in the medical domain. Therefore, this research aims to mitigate this gap by introducing ViMedAQA. This **Vi**etnamese **Med**ical **A**bstractive **Q**uestion-**A**nswering dataset covers four topics in the Vietnamese medical domain, including body parts, disease, drugs and medicine. Additionally, the empirical results on the proposed dataset examine the capability of the large language models in the Vietnamese medical domain, including reasoning, memorizing and awareness of essential information.- Anthology ID:
- 2024.acl-srw.31
- Volume:
- Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop)
- Month:
- August
- Year:
- 2024
- Address:
- Bangkok, Thailand
- Editors:
- Xiyan Fu, Eve Fleisig
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 356–364
- Language:
- URL:
- https://aclanthology.org/2024.acl-srw.31
- DOI:
- Cite (ACL):
- Minh-Nam Tran, Phu-Vinh Nguyen, Long Nguyen, and Dien Dinh. 2024. ViMedAQA: A Vietnamese Medical Abstractive Question-Answering Dataset and Findings of Large Language Model. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop), pages 356–364, Bangkok, Thailand. Association for Computational Linguistics.
- Cite (Informal):
- ViMedAQA: A Vietnamese Medical Abstractive Question-Answering Dataset and Findings of Large Language Model (Tran et al., ACL 2024)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2024.acl-srw.31.pdf