Conference Paper Published

PolyuCBS at SMM4H 2024: LLM-based Medical Disorder and Adverse Drug Event Detection with Low-rank Adaptation

Zhai, Y., Bao, X., Chersoni, E., Portelli, B., Lee, S. Y. M., Gu, J., & Huang, C.-R. (2024). PolyuCBS at SMM4H 2024: LLM-based Medical Disorder and Adverse Drug Event Detection with Low-rank Adaptation. In Proceedings of The ACL Social Media Mining for Health Research and Applications (SMM4H 2024) Workshop and Shared Tasks, 74-78.

URL: https://aclanthology.org/2024.smm4h-1.17/

Abstract

This is the demonstration of systems and results of our team’s participation in the Social Medical Mining for Health (SMM4H) 2024 Shared Task. Our team participated in two tasks: Task 1 and Task 5. Task 5 requires the detection of tweet sentences that claim children’s medical disorders from certain users. Task 1 needs teams to extract and normalize Adverse Drug Event terms in the tweet sentence. The team selected several Pre-trained Language Models and generative Large Language Models to meet the requirements. Strategies to improve the performance include cloze test, prompt engineering, Low Rank Adaptation etc. The test result of our system has an F1 score of 0.935, Precision of 0.954 and Recall of 0.917 in Task 5 and an overall F1 score of 0.08 in Task 1.