Summary of 1024m at Smm4h 2024: Tasks 3, 5 & 6 — Ensembles Of Transformers and Large Language Models For Medical Text Classification, by Ram Mohan Rao Kadiyala et al.
1024m at SMM4H 2024: Tasks 3, 5 & 6 – Ensembles of Transformers and Large Language Models for Medical Text Classification
by Ram Mohan Rao Kadiyala, M.V.P. Chandra Sekhara Rao
First submitted to arxiv on: 21 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper presents various approaches using Transformers and Large Language Models and their ensembles for tasks in the Social Media Mining for Health’24 challenge. Specifically, it investigates the performance of these models on three tasks: classifying texts on the impact of nature and outdoor spaces on mental health, binary classification of tweets reporting children’s health disorders, and binary classification of users self-reporting their age. The paper highlights the advantages and drawbacks of each approach for these tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper uses big language models to understand how people talk about their health on social media. It tries different ways to use these models for three specific tasks: figuring out if someone’s mental health is affected by nature, identifying tweets that mention kids’ health problems, and categorizing users based on their age. The goal is to see which approach works best for each task. |
Keywords
* Artificial intelligence * Classification
