# Can Large Language Models perform Relation-based Argument Mining?

arXiv: https://arxiv.org/abs/2402.11243 · HTML: https://mlsanity.com/paper/2402.11243/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 46 references that are in the map.
- First posted on arXiv 17 Feb 2024.

## Builds on (most cited first, 11 of 11)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers](https://mlsanity.com/paper/2210.17323/) (2022)
- [A Comprehensive Capability Analysis of GPT-3 and GPT-3.5 Series Models](https://mlsanity.com/paper/2303.10420/) (2023)
- [Parsing Argumentation Structures in Persuasive Essays](https://mlsanity.com/paper/1604.07370/) (2016)
- [Cross-topic Argument Mining from Heterogeneous Sources Using Attention-based Neural Networks](https://mlsanity.com/paper/1802.05758/) (2018)
- [Exploring the Potential of Large Language Models in Computational Argumentation](https://mlsanity.com/paper/2311.09022/) (2023)
- [Corpus Wide Argument Mining -- a Working Solution](https://mlsanity.com/paper/1911.10763/) (2019)
- [Transformer-Based Models for Automatic Identification of Argument Relations: A Cross-Domain Evaluation](https://mlsanity.com/paper/2011.13187/) (2020)
- [Classifying Argumentative Relations Using Logical Mechanisms and Argumentation Schemes](https://mlsanity.com/paper/2105.07571/) (2021)
- [Will It Blend? Mixing Training Paradigms & Prompting for Argument Quality Prediction](https://mlsanity.com/paper/2209.08966/) (2022)
- [How to Handle Different Types of Out-of-Distribution Scenarios in Computational Argumentation? A Comprehensive and Fine-Grained Field Study](https://mlsanity.com/paper/2309.08316/) (2023)

## Built on by (most cited first, 23 of 23)

- [PeerArg: Argumentative Peer Review with LLMs](https://mlsanity.com/paper/2409.16813/) (2024)
- [Retrieval- and Argumentation-Enhanced Multi-Agent LLMs for Judgmental Forecasting (Extended Version with Supplementary Material)](https://mlsanity.com/paper/2510.24303/) (2025)
- [PRISM: A Methodology for Auditing Biases in Large Language Models](https://mlsanity.com/paper/2410.18906/) (2024)
- [Leveraging Small LLMs for Argument Mining in Education: Argument Component Identification, Classification, and Assessment](https://mlsanity.com/paper/2502.14389/) (2025)
- [LLMs for Argument Mining: Detection, Extraction, and Relationship Classification of pre-defined Arguments in Online Comments](https://mlsanity.com/paper/2505.22956/) (2025)
- [A Methodology for Incompleteness-Tolerant and Modular Gradual Semantics for Argumentative Statement Graphs](https://mlsanity.com/paper/2410.22209/) (2024)
- [AMELIA: A Family of Multi-task End-to-end Language Models for Argumentation](https://mlsanity.com/paper/2508.17926/) (2025)
- [On the Suitability of pre-trained foundational LLMs for Analysis in German Legal Education](https://mlsanity.com/paper/2412.15902/) (2024)
- [Assisted Debate Builder with Large Language Models](https://mlsanity.com/paper/2405.13015/) (2024)
- [Compact Prompting in Instruction-tuned LLMs for Joint Argumentative Component Detection](https://mlsanity.com/paper/2603.03095/) (2026)
- [Evaluating LLM-Driven Summarisation of Parliamentary Debates with Computational Argumentation](https://mlsanity.com/paper/2604.19331/) (2026)
- [Language is Scary when Over-Analyzed: Unpacking Implied Misogynistic Reasoning with Argumentation Theory-Driven Prompts](https://mlsanity.com/paper/2409.02519/) (2024)
- [Multi-User Personalisation in Human-Robot Interaction: Resolving Preference Conflicts Using Gradual Argumentation](https://mlsanity.com/paper/2511.03576/) (2025)
- [AI-Assisted Authoring for Transparent, Data-Driven Documents](https://mlsanity.com/paper/2601.06027/) (2025)
- [LLM-based Argument Mining meets Argumentation and Description Logics: a Unified Framework for Reasoning about Debates](https://mlsanity.com/paper/2603.02858/) (2026)
- [Double Rectified Linear Unit-based Modular Semantics for Quantitative Bipolar Argumentation Framework](https://mlsanity.com/paper/2605.02551/) (2026)
- [Towards Robust Argumentative Essay Understanding via TIDE: An Interactive Framework with Trial and Debate](https://mlsanity.com/paper/2605.17247/) (2026)
- [LLM-INSTRUCT at UZH Shared Task 2026: Constraint-Aware Retrieval and Selective Debate for Paragraph-Level Argument Mining](https://mlsanity.com/paper/2607.20430/) (2026)
- [Argumentation for Common Ground: Finding Zones of Possible Agreement between Individuals in Conflict](https://mlsanity.com/paper/2608.15634/) (2026)
- [Embedding Models for Stance-Aware Argument Retrieval](https://mlsanity.com/paper/2608.28283/) (2026)
- [Who Argues What? Joint Argument-Entity Detection and Classification in Political Debates](https://mlsanity.com/paper/2609.10192/) (2026)
- [Extracting Arguments, Not Just Classifying Them: Instruction-Tuned LLMs for Generative Component Detection](https://mlsanity.com/paper/2609.24855/) (2026)
- [Argument Structure Prediction in Online Conversations: A Comparative Study of Modeling Paradigms and Task Architectures](https://mlsanity.com/paper/2609.39225/) (2026)

## Abstract (arXiv)

Argument mining (AM) is the process of automatically extracting arguments, their components and/or relations amongst arguments and components from text. As the number of platforms supporting online debate increases, the need for AM becomes ever more urgent, especially in support of downstream tasks. Relation-based AM (RbAM) is a form of AM focusing on identifying agreement (support) and disagreement (attack) relations amongst arguments. RbAM is a challenging classification task, with existing methods failing to perform satisfactorily. In this paper, we show that general-purpose Large Language Models (LLMs), appropriately primed and prompted, can significantly outperform the best performing (RoBERTa-based) baseline. Specifically, we experiment with two open-source LLMs (Llama-2 and Mistral) with ten datasets.