Good Data, Large Data, or No Data? Comparing Three Approaches in
  Developing Research Aspect Classifiers for Biomedical Papers
Shreya Chandrasekhar Chieh-Yang Huang Ting-Hao 'Kenneth' Huang
The rapid growth of scientific publications, particularly during the COVID-19
pandemic, emphasizes the need for tools to help researchers efficiently
comprehend the latest advancements. One essential part of understanding
scientific literature is research aspect classification, which categorizes
sentences in abstracts to Background, Purpose, Method, and Finding. In this
study, we investigate the impact of different datasets on model performance for
the crowd-annotated CODA-19 research aspect classification task. Specifically,
we explore the potential benefits of using the large, automatically curated
PubMed 200K RCT dataset and evaluate the effectiveness of large language models
(LLMs), such as LLaMA, GPT-3, ChatGPT, and GPT-4. Our results indicate that
using the PubMed 200K RCT dataset does not improve performance for the CODA-19
task. We also observe that while GPT-4 performs well, it does not outperform
the SciBERT model fine-tuned on the CODA-19 dataset, emphasizing the importance
of a dedicated and task-aligned datasets dataset for the target task. Our code
is available at https://github.com/Crowd-AI-Lab/CODA-19-exp.