Abstract
The following is a description of the RIGA team’s submissions for the SMM4H-2024 Task 1: Extraction and normalization of adverse drug events (ADEs) in English tweets. Our approach focuses on utilizing Large Language Models (LLMs) to generate data that enhances the fine-tuning of classification and Named Entity Recognition (NER) models. Our solution significantly outperforms mean and median submissions of other teams. The efficacy of our ADE extraction from tweets is comparable to the current state-of-the-art solution, established as the task baseline. The code for our method is available on GitHub (https://github.com/emukans/smm4h2024-riga)- Anthology ID:
- 2024.smm4h-1.6
- Volume:
- Proceedings of The 9th Social Media Mining for Health Research and Applications (SMM4H 2024) Workshop and Shared Tasks
- Month:
- August
- Year:
- 2024
- Address:
- Bangkok, Thailand
- Editors:
- Dongfang Xu, Graciela Gonzalez-Hernandez
- Venues:
- SMM4H | WS
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 23–27
- Language:
- URL:
- https://aclanthology.org/2024.smm4h-1.6
- DOI:
- Cite (ACL):
- Eduards Mukans and Guntis Barzdins. 2024. RIGA at SMM4H-2024 Task 1: Enhancing ADE discovery with GPT-4. In Proceedings of The 9th Social Media Mining for Health Research and Applications (SMM4H 2024) Workshop and Shared Tasks, pages 23–27, Bangkok, Thailand. Association for Computational Linguistics.
- Cite (Informal):
- RIGA at SMM4H-2024 Task 1: Enhancing ADE discovery with GPT-4 (Mukans & Barzdins, SMM4H-WS 2024)
- PDF:
- https://preview.aclanthology.org/landing_page/2024.smm4h-1.6.pdf