# In-Context Learning and Fine-Tuning GPT for Argument Mining

arXiv: https://arxiv.org/abs/2406.06699 · HTML: https://mlsanity.com/paper/2406.06699/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 15 references that are in the map.
- First posted on arXiv 10 Jun 2024.

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

- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [Self-Consistency Improves Chain of Thought Reasoning in Language Models](https://mlsanity.com/paper/2203.11171/) (2022)
- [A Survey of Large Language Models](https://mlsanity.com/paper/2303.18223/) (2023)
- [Emergent Abilities of Large Language Models](https://mlsanity.com/paper/2206.07682/) (2022)
- [Capabilities of GPT-4 on Medical Challenge Problems](https://mlsanity.com/paper/2303.13375/) (2023)
- [Can Generalist Foundation Models Outcompete Special-Purpose Tuning? Case Study in Medicine](https://mlsanity.com/paper/2311.16452/) (2023)
- [Parsing Argumentation Structures in Persuasive Essays](https://mlsanity.com/paper/1604.07370/) (2016)

## Built on by (most cited first, 3 of 3)

- [AMELIA: A Family of Multi-task End-to-end Language Models for Argumentation](https://mlsanity.com/paper/2508.17926/) (2025)
- [LLM-based Argument Mining meets Argumentation and Description Logics: a Unified Framework for Reasoning about Debates](https://mlsanity.com/paper/2603.02858/) (2026)
- [Who Argues What? Joint Argument-Entity Detection and Classification in Political Debates](https://mlsanity.com/paper/2609.10192/) (2026)

## Abstract (arXiv)

Large Language Models (LLMs) have become ubiquitous in NLP and deep learning. In-Context Learning (ICL) has been suggested as a bridging paradigm between the training-free and fine-tuning LLMs settings. In ICL, an LLM is conditioned to solve tasks by means of a few solved demonstration examples included as prompt. Argument Mining (AM) aims to extract the complex argumentative structure of a text, and Argument Type Classification (ATC) is an essential sub-task of AM. We introduce an ICL strategy for ATC combining kNN-based examples selection and majority vote ensembling. In the training-free ICL setting, we show that GPT-4 is able to leverage relevant information from only a few demonstration examples and achieve very competitive classification accuracy on ATC. We further set up a fine-tuning strategy incorporating well-crafted structural features given directly in textual form. In this setting, GPT-3.5 achieves state-of-the-art performance on ATC. Overall, these results emphasize the emergent ability of LLMs to grasp global discursive flow in raw text in both off-the-shelf and fine-tuned setups.