Jul 2026· Machine Learning and Knowledge Extraction· Vol 8, pp. 203· 0 citations· 77 references
Abstract
We propose a modular, retrieval-augmented pipeline for computational argumentation that integrates two complementary components: ArgStance, a multi-task model for argument and stance reasoning, and TargetMatch, a contrastive retrieval model that treats target identification as a first-class retrieval task. We further formulate stance detection as a framing-aware problem, recognizing that the polarity of a stance toward a target depends on how the proposition is framed. To support broad generalization, we construct a large dataset spanning Kialo discussions, Wikipedia, and curated news articles, and introduce a cross-source injection strategy that mitigates domain and style biases. Our compact models achieve F1 scores of 0.94 for argument detection (ModernBERT-base) and 0.84 for same-side stance detection (ModernBERT-large), while TargetMatch attains a top-10 retrieval accuracy of 0.75. Under controlled zero-shot comparisons with large language models, our models remain competitive while offering advantages in reproducibility, deployment cost, and controllable intermediate predictions.
This paper introduces diagnostic word-ablation metrics to quantify this phenomenon and proposes a data-centric solution that can alleviate the observed overcorrection in stance-aware argument retrieval and demonstrates that, for sufficiently powerful models, this approach can alleviate the observed overcorrection.
Angelo Sparacino, Francesca Toni, Adam Dejl· 0 citations
This work presents MINE (Meta-prompting and In-context N-shot Examples), a simple, light-weight, training-free framework that first creates a target-specific LLM stance agent through meta-prompting, and then augments it with a small number of semantically retrieved examples.
Uwaila Ekhator, Francesca Spezzano· International Journal of Dat...· 0 citations
This work introduces a guided retrieval-augmented methodology for fallacy detection and classification that leverages argumentative relations of support and attack to dynamically steer the extraction of relevant documents when retrieval is argumentatively guided.
Deborah Dore, Greta Damo, Elena Cabrio et al.· 0 citations
GEM is presented, a generative embedding model that augments retrieval through its own knowledge by explicitly reasoning about user intent and relevance criteria, and its generative nature allows test-time compute scaling via prompting to further enhance retrieval performance.
This work proposes Factorized Hypothesis Search (FHS), which maintains multiple partial interpretations over named semantic dimensions that support structured query rendering, multi-hypothesis retrieval, and dimension-level candidate verification.
Lin-Hai Ma, Ethan F. Wei, Xue-Qing Peng et al.· 0 citations
Stance detection underpins a wide range of large-scale data analytics applications on social media, including opinion monitoring, policy evaluation, and misinformation tracking. While prompting-based methods leveraging Large Language Models (LLMs) have achieved strong zero-shot performance, they invariably perform per-...
Qiang Xie, Yan-Qing Ye, Xiao-Dong Wang et al.· 2026 12th International Conf...· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
