# Large Language Models in Argument Mining: A Survey

arXiv: https://arxiv.org/abs/2506.16383 · HTML: https://mlsanity.com/paper/2506.16383/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Placed in RAG robustness and adaptive retrieval, one of 15 directions in Retrieval-augmented generation.
- Builds on 85 of its 207 references that are in the map.
- First posted on arXiv 19 Jun 2025.

## Placed in

- Retrieval-augmented generation / [RAG robustness and adaptive retrieval](https://mlsanity.com/rag/rag-robustness-and-adaptive-retrieval/)

## Builds on (most cited first, 30 of 85)

- [Scaling Instruction-Finetuned Language Models](https://mlsanity.com/paper/2210.11416/) (2022)
- [A Survey on Bias and Fairness in Machine Learning](https://mlsanity.com/paper/1908.09635/) (2019)
- [On LLMs-Driven Synthetic Data Generation, Curation, and Evaluation: A Survey](https://mlsanity.com/paper/2406.15126/) (2024)
- [The 'Problem' of Human Label Variation: On Ground Truth in Data, Modeling and Evaluation](https://mlsanity.com/paper/2211.02570/) (2022)
- [Winning Arguments: Interaction Dynamics and Persuasion Strategies in Good-faith Online Discussions](https://mlsanity.com/paper/1602.01103/) (2016)
- [Seeing Things from a Different Angle: Discovering Diverse Perspectives about Claims](https://mlsanity.com/paper/1906.03538/) (2019)
- [Argumentation Mining in User-Generated Web Discourse](https://mlsanity.com/paper/1601.02403/) (2016)
- [A Large-scale Dataset for Argument Quality Ranking: Construction and Analysis](https://mlsanity.com/paper/1911.11408/) (2019)
- [From Arguments to Key Points: Towards Automatic Argument Summarization](https://mlsanity.com/paper/2005.01619/) (2020)
- [Classification and Clustering of Arguments with Contextualized Word Embeddings](https://mlsanity.com/paper/1906.09821/) (2019)
- [Automatic Argument Quality Assessment -- New Datasets and Methods](https://mlsanity.com/paper/1909.01007/) (2019)
- [ArguGPT: evaluating, understanding and identifying argumentative essays generated by GPT models](https://mlsanity.com/paper/2304.07666/) (2023)
- [Exploring the Potential of Large Language Models in Computational Argumentation](https://mlsanity.com/paper/2311.09022/) (2023)
- [Argument Mining for Understanding Peer Reviews](https://mlsanity.com/paper/1903.10104/) (2019)
- [AMPERSAND: Argument Mining for PERSuAsive oNline Discussions](https://mlsanity.com/paper/2004.14677/) (2020)
- [Corpus Wide Argument Mining -- a Working Solution](https://mlsanity.com/paper/1911.10763/) (2019)
- [Argument Strength is in the Eye of the Beholder: Audience Effects in Persuasion](https://mlsanity.com/paper/1708.09085/) (2017)
- [Evidence-aware Fake News Detection with Graph Neural Networks](https://mlsanity.com/paper/2201.06885/) (2022)
- [Are You Convinced? Choosing the More Convincing Evidence with a Siamese Network](https://mlsanity.com/paper/1907.08971/) (2019)
- [Rumor Detection on Twitter with Claim-Guided Hierarchical Graph Attention Networks](https://mlsanity.com/paper/2110.04522/) (2021)
- [GENEVA: Benchmarking Generalizability for Event Argument Extraction with Hundreds of Event Types and Argument Roles](https://mlsanity.com/paper/2205.12505/) (2022)
- [ArgLegalSumm: Improving Abstractive Summarization of Legal Documents with Argument Mining](https://mlsanity.com/paper/2209.01650/) (2022)
- [Overview of the 2021 Key Point Analysis Shared Task](https://mlsanity.com/paper/2110.10577/) (2021)
- [IAM: A Comprehensive and Large-Scale Dataset for Integrated Argument Mining Tasks](https://mlsanity.com/paper/2203.12257/) (2022)
- [Multilingual Argument Mining: Datasets and Analysis](https://mlsanity.com/paper/2010.06432/) (2020)
- [Every Bite Is an Experience: Key Point Analysis of Business Reviews](https://mlsanity.com/paper/2106.06758/) (2021)
- [Claim Detection in Biomedical Twitter Posts](https://mlsanity.com/paper/2104.11639/) (2021)
- [ECon: On the Detection and Resolution of Evidence Conflicts](https://mlsanity.com/paper/2410.04068/) (2024)
- [Rhetoric, Logic, and Dialectic: Advancing Theory-based Argument Quality Assessment in Natural Language Processing](https://mlsanity.com/paper/2006.00843/) (2020)
- [Argument Mining Driven Analysis of Peer-Reviews](https://mlsanity.com/paper/2012.07743/) (2020)

## Built on by (most cited first, 14 of 14)

- [Argumentative Human-AI Decision-Making: Toward AI Agents That Reason With Us, Not For Us](https://mlsanity.com/paper/2603.15946/) (2026)
- [Retrieval-Augmented Generation Must Move Beyond Factual Grounding to Represent Diverse Opinions](https://mlsanity.com/paper/2604.12138/) (2026)
- [Arg-LLaDA: Argument Summarization via Large Language Diffusion Models and Sufficiency-Aware Refinement](https://mlsanity.com/paper/2507.19081/) (2025)
- [Designing and Evaluating Chain-of-Hints for Scientific Question Answering](https://mlsanity.com/paper/2510.21087/) (2025)
- [TruthStance: An Annotated Dataset of Conversations on Truth Social](https://mlsanity.com/paper/2602.14406/) (2026)
- [Compliance-by-Construction Argument Graphs: Using Generative AI to Produce Evidence-Linked Formal Arguments for Certification-Grade Accountability](https://mlsanity.com/paper/2604.04103/) (2026)
- [Structural Dilemmas and Developmental Pathways of Legal Argument Mining in the Era of Artificial Intelligence](https://mlsanity.com/paper/2605.02308/) (2026)
- [Graph-Augmented LLMs for Swiss MP Ideology Prediction](https://mlsanity.com/paper/2605.04643/) (2026)
- [A Reflective Storytelling Agent for Older Adults: Integrating Argumentation Schemes and Argument Mining in LLM-Based Personalised Narratives](https://mlsanity.com/paper/2605.10531/) (2026)
- [An LLM-Based System for Argument Mining](https://mlsanity.com/paper/2605.13793/) (2026)
- [GRASP: Deterministic argument ranking in interaction graphs](https://mlsanity.com/paper/2605.19141/) (2026)
- [TRACE: An Operational Reasoning Schema for Auditable Agentic Commitments](https://mlsanity.com/paper/2607.12480/) (2026)
- [Discourse-Aware Policy Analysis with Argumentation: A Hybrid LLM-Symbolic Framework for Disaster Governance](https://mlsanity.com/paper/2607.13260/) (2026)
- [ARGUS: Theory-of-Mind Guided Argument Generation with Strategy-Aware Planning and Knowledge Grounding](https://mlsanity.com/paper/2608.20405/) (2026)

## Abstract (arXiv)

Large Language Models (LLMs) have fundamentally reshaped Argument Mining (AM), shifting it from a pipeline of supervised, task-specific classifiers to a spectrum of prompt-driven, retrieval-augmented, and reasoning-oriented paradigms. Yet existing surveys largely predate this transition, leaving unclear how LLMs alter task formulations, dataset design, evaluation methodology, and the theoretical foundations of computational argumentation. In this survey, we synthesise research and provide the first unified account of AM in the LLM era. We revisit canonical AM subtasks, i.e., claim and evidence detection, relation prediction, stance classification, argument quality assessment, and argumentative summarisation, and show how prompting, chain-of-thought reasoning, and in-context learning blur traditional task boundaries. We catalogue the rapid evolution of resources, including integrated multi-layer corpora and LLM-assisted annotation pipelines that introduce new opportunities as well as risks of bias and evaluation circularity. Building on this mapping, we identify emerging architectural patterns across LLM-based AM systems and consolidate evaluation practices spanning component-level accuracy, soft-label quality assessment, and LLM-judge reliability. Finally, we outline persistent challenges, including long-context reasoning, multimodal and multilingual robustness, interpretability, and cost-efficient deployment, and propose a forward-looking research agenda for LLM-driven computational argumentation.