# Assessing Open-Source Large Language Models on Argumentation Mining Subtasks

arXiv: https://arxiv.org/abs/2411.05639 · HTML: https://mlsanity.com/paper/2411.05639/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Nov 2024.

## 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)
- [ArgBench: Benchmarking LLMs on Computational Argumentation Tasks](https://mlsanity.com/paper/2604.17366/) (2026)
- [CLEAR: A Comprehensive Linguistic Evaluation of Argument Rewriting by Large Language Models](https://mlsanity.com/paper/2509.15027/) (2025)

## Abstract (arXiv)

We explore the capability of four open-sourcelarge language models (LLMs) in argumentation mining (AM). We conduct experiments on three different corpora; persuasive essays(PE), argumentative microtexts (AMT) Part 1 and Part 2, based on two argumentation mining sub-tasks: (i) argumentative discourse units classifications (ADUC), and (ii) argumentative relation classification (ARC). This work aims to assess the argumentation capability of open-source LLMs, including Mistral 7B, Mixtral8x7B, LlamA2 7B and LlamA3 8B in both, zero-shot and few-shot scenarios. Our analysis contributes to further assessing computational argumentation with open-source LLMs in future research efforts.