# Efficient argument classification with compact language models and ChatGPT-4 refinements

arXiv: https://arxiv.org/abs/2403.15473 · HTML: https://mlsanity.com/paper/2403.15473/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 30 references that are in the map.
- First posted on arXiv 20 Mar 2024.

## Builds on (most cited first, 8 of 8)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Tree of Thoughts: Deliberate Problem Solving with Large Language Models](https://mlsanity.com/paper/2305.10601/) (2023)
- [Cross-topic Argument Mining from Heterogeneous Sources Using Attention-based Neural Networks](https://mlsanity.com/paper/1802.05758/) (2018)
- [Classification and Clustering of Arguments with Contextualized Word Embeddings](https://mlsanity.com/paper/1906.09821/) (2019)
- [Argument Mining with Structured SVMs and RNNs](https://mlsanity.com/paper/1704.06869/) (2017)
- [Transformer-Based Models for Automatic Identification of Argument Relations: A Cross-Domain Evaluation](https://mlsanity.com/paper/2011.13187/) (2020)
- [Argument Mining using BERT and Self-Attention based Embeddings](https://mlsanity.com/paper/2302.13906/) (2023)
- [Towards a Benchmark of Natural Language Arguments](https://mlsanity.com/paper/1405.0941/) (2014)

## Built on by (most cited first, 5 of 5)

- [The Moral Foundations Weibo Corpus](https://mlsanity.com/paper/2411.09612/) (2024)
- [A comprehensive study of LLM-based argument classification: from LLAMA through GPT-4o to Deepseek-R1](https://mlsanity.com/paper/2507.08621/) (2025)
- [Compact Prompting in Instruction-tuned LLMs for Joint Argumentative Component Detection](https://mlsanity.com/paper/2603.03095/) (2026)
- [A comprehensive study of LLM-based argument classification: from Llama through DeepSeek to GPT-5.2](https://mlsanity.com/paper/2603.19253/) (2026)
- [Extracting Arguments, Not Just Classifying Them: Instruction-Tuned LLMs for Generative Component Detection](https://mlsanity.com/paper/2609.24855/) (2026)

## Abstract (arXiv)

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.