Efficient argument classification with compact language models and
  ChatGPT-4 refinements
Marcin Pietron Rafał Olszowski Jakub Gomułka
Argument mining (AM) is defined as the task of automatically identifying and
extracting argumentative components (e.g. premises, claims, etc.) and detecting
the existing relations among them (i.e., support, attack, no relations). Deep
learning models enable us to analyze arguments more efficiently than
traditional methods and extract their semantics. This paper presents
comparative studies between a few deep learning-based models in argument
mining. The work concentrates on argument classification. The research was done
on a wide spectrum of datasets (Args.me, UKP, US2016). The main novelty of this
paper is the ensemble model which is based on BERT architecture and ChatGPT-4
as fine tuning model. The presented results show that BERT+ChatGPT-4
outperforms the rest of the models including other Transformer-based and
LSTM-based models. The observed improvement is, in most cases, greater than
10The presented analysis can provide crucial insights into how the models for
argument classification should be further improved. Additionally, it can help
develop a prompt-based algorithm to eliminate argument classification errors.