[2603.27451] Multi-Agent Dialectical Refinement for Enhanced Argument Classification

Computer Science > Computation and Language arXiv:2603.27451 (cs) [Submitted on 29 Mar 2026] Title:Multi-Agent Dialectical Refinement for Enhanced Argument Classification Authors:Jakub Bąba, Jarosław A. Chudziak View a PDF of the paper titled Multi-Agent Dialectical Refinement for Enhanced Argument Classification, by Jakub B\k{a}ba and Jaros{\l}aw A. Chudziak View PDF HTML (experimental) Abstract:Argument Mining (AM) is a foundational technology for automated writing evaluation, yet traditional supervised approaches rely heavily on expensive, domain-specific fine-tuning. While Large Language Models (LLMs) offer a training-free alternative, they often struggle with structural ambiguity, failing to distinguish between similar components like Claims and Premises. Furthermore, single-agent self-correction mechanisms often suffer from sycophancy, where the model reinforces its own initial errors rather than critically evaluating them. We introduce MAD-ACC (Multi-Agent Debate for Argument Component Classification), a framework that leverages dialectical refinement to resolve classification uncertainty. MAD-ACC utilizes a Proponent-Opponent-Judge model where agents defend conflicting interpretations of ambiguous text, exposing logical nuances that single-agent models miss. Evaluation on the UKP Student Essays corpus demonstrates that MAD-ACC achieves a Macro F1 score of 85.7%, significantly outperforming single-agent reasoning baselines, without requiring domain-specific training...