UQ at #SMM4H 2023: ALEX for Public Health Analysis with Social Media
This work addresses public health analysis from social media data, but it is incremental as it builds on existing LLM techniques with a novel explanation mechanism.
The paper tackled the challenges of high training costs and data imbalance in using large language models for public health analysis on social media by proposing the ALEX framework, which achieved the best performance in two tasks and a high score in another at the SMM4H 2023 competition.
As social media becomes increasingly popular, more and more activities related to public health emerge. Current techniques for public health analysis involve popular models such as BERT and large language models (LLMs). However, the costs of training in-domain LLMs for public health are especially expensive. Furthermore, such kinds of in-domain datasets from social media are generally imbalanced. To tackle these challenges, the data imbalance issue can be overcome by data augmentation and balanced training. Moreover, the ability of the LLMs can be effectively utilized by prompting the model properly. In this paper, a novel ALEX framework is proposed to improve the performance of public health analysis on social media by adopting an LLMs explanation mechanism. Results show that our ALEX model got the best performance among all submissions in both Task 2 and Task 4 with a high score in Task 1 in Social Media Mining for Health 2023 (SMM4H)[1]. Our code has been released at https:// github.com/YanJiangJerry/ALEX.