Large Language Models in Argument Mining: A Survey

• URL: http://arxiv.org/abs/2506.16383v5
• Date: Mon, 04 Aug 2025 04:51:04 GMT
• Title: Large Language Models in Argument Mining: A Survey
• Authors: Hao Li, Viktor Schlegel, Yizheng Sun, Riza Batista-Navarro, Goran Nenadic,
• Abstract summary: Argument Mining (AM) focuses on extracting argumentative structures from text.<n>The advent of Large Language Models (LLMs) has profoundly transformed AM, enabling advanced in-context learning.<n>This survey systematically synthesizes recent advancements in LLM-driven AM.
• Score: 15.041650203089057
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Argument Mining (AM), a critical subfield of Natural Language Processing (NLP), focuses on extracting argumentative structures from text. The advent of Large Language Models (LLMs) has profoundly transformed AM, enabling advanced in-context learning, prompt-based generation, and robust cross-domain adaptability. This survey systematically synthesizes recent advancements in LLM-driven AM. We provide a concise review of foundational theories and annotation frameworks, alongside a meticulously curated catalog of datasets. A key contribution is our comprehensive taxonomy of AM subtasks, elucidating how contemporary LLM techniques -- such as prompting, chain-of-thought reasoning, and retrieval augmentation -- have reconfigured their execution. We further detail current LLM architectures and methodologies, critically assess evaluation practices, and delineate pivotal challenges including long-context reasoning, interpretability, and annotation bottlenecks. Conclusively, we highlight emerging trends and propose a forward-looking research agenda for LLM-based computational argumentation, aiming to strategically guide researchers in this rapidly evolving domain.

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