Improving End-to-End Argument Mining with LLMs via Multi-Candidate Reranking and Bounded Repair

Abstract

End-to-end Argument Mining (AM) aims to extract structured argument graphs directly from raw text by jointly identifying components and their rhetorical relations. Large Language Models (LLMs) have shown promise for this complex task by formulating it as direct text-to-graph generation. However, since most current LLM-based approaches rely on a single-pass generation paradigm, they struggle to handle the tight coupling of AM subtasks. Consequently, early extraction errors easily cascade, resulting in fragmented relations and globally inconsistent structures. Critically, these single-pass models lack mechanisms to backtrack and correct upstream mistakes when downstream inconsistencies arise. To address this problem, we propose the Multi-Candidate Reranking and Bounded Repair (MCR-BR) framework for LLM-based end-to-end AM. Instead of committing to a single output, our pipeline first generates diverse component candidates to preserve alternative hypotheses. It then constructs full argument graphs for these candidates and ranks them using a consistency-guided scoring strategy based on structural completeness, semantic coherence, and validity. For any remaining structural anomalies, a bounded local correction loop actively repairs the graph. We further introduce a targeted optimization module for conclusion-like components, which are common extraction bottlenecks. Experiments on the AAEC and CDCP benchmarks demonstrate that the MCR-BR framework consistently improves in-domain extraction and exhibits potential for cross-domain generalization.