mlsanityData to 7 Oct 2026

Can Large Language Models perform Relation-based Argument Mining?

arXiv 2402.11243 ↗First posted 17 Feb 2024cs.CL cs.AIcited by 23

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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.

Key facts. Cited by 23 papers in the map (data through 7 Oct 2026). Builds on 11 of its 46 references that are in the map. First posted on arXiv 17 Feb 2024.

Builds on

11 of 46 references.

References · 11

  1. 2020Language Models are Few-Shot Learnerscited by 32,199
  2. 2022GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformerscited by 1,838
  3. 2023A Comprehensive Capability Analysis of GPT-3 and GPT-3.5 Series Modelscited by 244
  4. 2016Parsing Argumentation Structures in Persuasive Essayscited by 99
  5. 2018Cross-topic Argument Mining from Heterogeneous Sources Using Attention-based Neural Networkscited by 58
  6. 2023Exploring the Potential of Large Language Models in Computational Argumentationcited by 42
  7. 2019Corpus Wide Argument Mining -- a Working Solutioncited by 36
  8. 2020Transformer-Based Models for Automatic Identification of Argument Relations: A Cross-Domain Evaluationcited by 20
  9. 2021Classifying Argumentative Relations Using Logical Mechanisms and Argumentation Schemescited by 17
  10. 2022Will It Blend? Mixing Training Paradigms & Prompting for Argument Quality Predictioncited by 7
  11. 2023How to Handle Different Types of Out-of-Distribution Scenarios in Computational Argumentation? A Comprehensive and Fine-Grained Field Studycited by 4

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 6; 2025: 6; 2026: 11.

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  1. 2024PeerArg: Argumentative Peer Review with LLMscited by 9
  2. 2025Retrieval- and Argumentation-Enhanced Multi-Agent LLMs for Judgmental Forecasting (Extended Version with Supplementary Material)cited by 8
  3. 2024PRISM: A Methodology for Auditing Biases in Large Language Modelscited by 4
  4. 2025Leveraging Small LLMs for Argument Mining in Education: Argument Component Identification, Classification, and Assessmentcited by 4
  5. 2025LLMs for Argument Mining: Detection, Extraction, and Relationship Classification of pre-defined Arguments in Online Commentscited by 4
  6. 2024A Methodology for Incompleteness-Tolerant and Modular Gradual Semantics for Argumentative Statement Graphscited by 2
  7. 2025AMELIA: A Family of Multi-task End-to-end Language Models for Argumentationcited by 2
  8. 2024Assisted Debate Builder with Large Language Modelscited by 1
  9. 2024On the Suitability of pre-trained foundational LLMs for Analysis in German Legal Educationcited by 1
  10. 2026Compact Prompting in Instruction-tuned LLMs for Joint Argumentative Component Detectioncited by 1
  11. 2026Evaluating LLM-Driven Summarisation of Parliamentary Debates with Computational Argumentationcited by 1
  12. 2024Language is Scary when Over-Analyzed: Unpacking Implied Misogynistic Reasoning with Argumentation Theory-Driven Promptscited by 0
11 more
  1. 2025Multi-User Personalisation in Human-Robot Interaction: Resolving Preference Conflicts Using Gradual Argumentationcited by 0
  2. 2025AI-Assisted Authoring for Transparent, Data-Driven Documentscited by 0
  3. 2026LLM-based Argument Mining meets Argumentation and Description Logics: a Unified Framework for Reasoning about Debatescited by 0
  4. 2026Double Rectified Linear Unit-based Modular Semantics for Quantitative Bipolar Argumentation Frameworkcited by 0
  5. 2026Towards Robust Argumentative Essay Understanding via TIDE: An Interactive Framework with Trial and Debatecited by 0
  6. 2026LLM-INSTRUCT at UZH Shared Task 2026: Constraint-Aware Retrieval and Selective Debate for Paragraph-Level Argument Miningcited by 0
  7. 2026Argumentation for Common Ground: Finding Zones of Possible Agreement between Individuals in Conflictcited by 0
  8. 2026Embedding Models for Stance-Aware Argument Retrievalcited by 0
  9. 2026Who Argues What? Joint Argument-Entity Detection and Classification in Political Debatescited by 0
  10. 2026Extracting Arguments, Not Just Classifying Them: Instruction-Tuned LLMs for Generative Component Detectioncited by 0
  11. 2026Argument Structure Prediction in Online Conversations: A Comparative Study of Modeling Paradigms and Task Architecturescited by 0