The paper investigates how dense embedding models can be used for stance-aware argument retrieval, a task that requires both topic relevance and correct stance (support or attack) toward a claim. Experiments reveal that current models favor topical overlap and ignore stance, and that contrastive training to fix this bias leads to over-correction, where models focus too much on polarity keywords at the expense of topic relevance. To address this, the authors propose diagnostic word-ablation metrics and a data‑centric solution involving a balanced argument curriculum and LLM‑augmented stance‑inverted arguments, which helps powerful models learn deeper directional logic and improves stance‑aware retrieval performance.
Political debates are often analyzed through Argument Mining (AM) to investigate the key arguments that drive them. However, political arguments are rarely interpretable from argumentative spans alone...
The paper introduces DNE‑ElecDeb, an enriched version of the USElecDeb dataset that annotates Debate Named Entities (DNEs) in both argumentative and non‑argumentative spans, and defines Debate Named Entity Recognition (DNER) as a new task. It proposes Joint Argument and Entity Tagging (JAET), a generative framework that fine‑tunes decoder‑only LLMs to insert inline argument and entity tags into debate turns while preserving the original transcript. JAET achieves significant improvements in joint AM+DNER performance (+27.3% relative F1 in the untyped setting and +41.9% in the typed setting) over sequential pipelines, and these gains generalize to Persuasive Essays (+26.6% and +52.7%).
MMDS-Bench is a new diagnostic benchmark for multimodal dynamic stance classification in social media parent‑reply interactions. It contains 3,482 multimodal instances annotated with a seven‑label stance taxonomy, plus an 800‑instance subset that demands structured reasoning over parent and reply understanding and stance‑relation inference. The benchmark also tags each instance with five challenge factors—multimodal fusion, parent framing, non‑literal expression, interaction reasoning, and label‑boundary ambiguity—and evaluates 12 multimodal large language models using a reference‑grounded LLM‑judge protocol, revealing that current models still struggle with relational inference beyond separate parent and reply comprehension.
MoPrO‑SD is a stance‑specific prompt optimization framework that breaks prompts into semantic modules and refines them sequentially with LLM feedback. It evaluates three refinement strategies—instruction‑only, instruction with reasoning, and instruction with per‑example reasoning—using class‑conditioned errors to improve stance detection. The authors also release VaccineStance, a new human‑annotated benchmark for vaccine stance detection, and demonstrate that MoPrO‑SD outperforms human‑written base prompts across multiple datasets and open‑weight LLMs.
arXiv:2606. 24976v1 Announce Type: cross Abstract: Foundation-model agents in multi-step, open-ended environments frequently suffer from compounding errors, where early mistakes contaminate long-horizon trajectories.
The paper introduces a retrieval‑augmented framework for detecting and classifying fallacies in political debate transcripts. By dynamically retrieving documents guided by argumentative relations of support and attack, the method leverages external knowledge to improve performance. Experiments on the ElecDeb60to20 benchmark show significant gains, raising macro‑F1 to 0.864 for detection and 0.725 for classification compared to non‑retrieval baselines.
The paper introduces a stance‑granularity selection policy for dialog systems that engage users with alternative viewpoints. By using Perspectivized Stance Vectors to break positions into concept‑level stances, the system selects the most novel viewpoint within a bandit‑controlled distance bound. Experiments with 63 participants show that concept‑level stance yields more varied exposure sequences and broader coverage of topic positions, though it does not affect depth of justification or reconstruction accuracy.
arXiv:2604. 00878v2 Announce Type: replace-cross Abstract: Actor-level stance detection aims to determine an author expressed position toward specific geopolitical actors mentioned or implicated in a text.
arXiv:2607. 10562v1 Announce Type: new Abstract: Evaluating the multi-hop reasoning capabilities of large language models remains a significant challenge.
arXiv:2610.15091v1 Announce Type: cross Abstract: Argument structures represent how individual arguments interact within a discourse. However, leveraging these structures to infer meaningful knowledg...
arXiv:2607. 24191v1 Announce Type: cross Abstract: Conversational stance detection has shifted from static text analysis to dynamic multimodal modeling.
arXiv:2406. 14657v4 Announce Type: replace-cross Abstract: We introduce OpenDebateEvidence, a comprehensive dataset for argument mining and summarization sourced from the American Competitive Debate community.