Reproduce “Large Language Models in Argument Mining: A Survey” on OpenResearch

About this paper

Hao Li, Viktor Schlegel, Yizheng Sun, Riza Batista-Navarro, Goran Nenadic

This survey from the University of Manchester synthesizes the impact of Large Language Models on Argument Mining, detailing how LLMs transform methodologies, datasets, and evaluation, while also outlining challenges and a future research agenda for the field.

Key insights

• Large Language Models have fundamentally shifted Argument Mining from supervised task-specific pipelines to prompt-driven, retrieval-augmented, and reasoning-oriented paradigms, blurring traditional task boundaries.
• LLMs are increasingly utilized for dataset creation and annotation, including synthetic data generation and LLM-assisted labeling, but this introduces new concerns regarding bias, stylistic homogenization, and evaluation circularity.
• The integration of LLMs necessitates a more holistic and theory-aware research approach, requiring better interpretability, bias mitigation, robustness, and deeper integration of argumentation theory into model objectives.

Summary by alphaXiv.