1024m at SMM4H 2024: Tasks 3, 5 & 6 -- Ensembles of Transformers and Large Language Models for Medical Text Classification

TL;DR

This paper explores ensemble methods of Transformers and Large Language Models to improve medical text classification tasks on social media data, highlighting their performance, advantages, and limitations.

Contribution

It introduces ensemble approaches of Transformers and LLMs tailored for specific SMM4H'24 classification tasks, demonstrating their effectiveness and analyzing their pros and cons.

Findings

Ensemble models outperform individual models in classification accuracy.

Transformers and LLMs show strengths in handling social media medical texts.

Trade-offs include computational cost and model interpretability.

Abstract

Social media is a great source of data for users reporting information and regarding their health and how various things have had an effect on them. This paper presents various approaches using Transformers and Large Language Models and their ensembles, their performance along with advantages and drawbacks for various tasks of SMM4H'24 - Classifying texts on impact of nature and outdoor spaces on the author's mental health (Task 3), Binary classification of tweets reporting their children's health disorders like Asthma, Autism, ADHD and Speech disorder (task 5), Binary classification of users self-reporting their age (task 6).