ARGUS: Towards End-to-End Argument Mining with Large Language Models

Ettore Caputo; Sergio Greco; Lucio La Cava

AAAI 2026technical0 citations

ARGUS: Towards End-to-End Argument Mining with Large Language Models

Ettore Caputo, Sergio Greco, Lucio La Cava

Abstract

We present ARGUS, an end-to-end Argument Mining (AM) tool that exploits Large Language Models (LLMs) to automatically perform all core AM tasks, i.e., Argument Component Segmentation, Classification, Relation Identification, and Relation Classification. Furthermore, ARGUS builds the corresponding argumentation framework (AF) and seamlessly integrates symbolic solvers to compute extensions and perform formal reasoning. ARGUS is designed to ensure broad flexibility and usability, supporting any open-source or commercial LLMs and symbolic solvers, providing a ready-to-use platform for exploring neuro-symbolic approaches to argumentation in both research and practical applications.

BibTeX

@inproceedings{aaai2026_argustowardsendt,
  title = {ARGUS: Towards End-to-End Argument Mining with Large Language Models},
  author = {Ettore Caputo and Sergio Greco and Lucio La Cava},
  booktitle = {AAAI 2026},
  year = {2026}
}