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...
arXiv:2508. 03250v4 Announce Type: replace-cross Abstract: The increasing amount of political debates and politics-related discussions calls for the definition of novel computational methods to automatically analyse such content with the final goal of lightening up political deliberation to citizens.
MABPD (Multi‑Agent Bias Probing & Detection) is a training‑free pipeline that uses three specialized large language model agents to analyze news articles from complementary perspectives and resolve disagreements via a Structured Argument Debate (SAD) protocol. SAD imposes an asymmetric burden of proof—biased claims lacking grounded textual evidence receive zero weight—along with role‑weighted voting and post‑consensus verification, replacing task‑specific supervised decision boundaries. Ablation studies show that the debate module alone accounts for up to a 10.6‑point F1 gain, and on the BABE benchmark MABPD attains 83.4% macro F1, within 0.7 percentage points of the supervised state‑of‑the‑art, while achieving 75.0% zero‑shot accuracy on the SemEval 2019 HyperPartisan corpus.
arXiv:2606.12186v2 Announce Type: replace Abstract: Enthymemes, arguments with unstated premises or conclusions, are pervasive in persuasive discourse, yet their annotation remains notoriously subjec...
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.
Enthymemes, arguments with unstated premises or conclusions, are pervasive in persuasive discourse, yet their annotation remains notoriously subjective. We present a resource of 1,482 tweets from politically controversial discourse, annotated by five annotators for the presence of enthymemes and their argument structure, designed to study label variation.
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.
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.
arXiv:2606. 01736v1 Announce Type: cross Abstract: As LLMs are increasingly used to draft public-facing arguments, they may flatten public debate by repeatedly introducing the same polished, plausible arguments.
The paper introduces “Persuasio”, a multi‑agent dialogue platform that uses a formal argumentation theory to adjudicate winners in free‑text debates. Using this system, the authors generated 192 debates on a UK political topic involving humans and large language models (LLMs), and evaluated 22 interlocutors through automated adjudication and 9,702 crowdsourced pairwise judgments across 1,386 annotation instances. The results show a consistent decoupling between subjective persuasiveness—where LLMs dominate—and formal argumentative strength—where humans remain competitive, with multi‑agent and retrieval‑augmented variants widening this gap.
arXiv:2607. 15095v1 Announce Type: cross Abstract: The formation of political coalitions is a complex negotiation driven by both concrete policy objectives and deep-seated ideological convictions.
arXiv:2603. 23841v2 Announce Type: replace-cross Abstract: While Large Language Models (LLMs) are increasingly used as primary sources of information, their potential for political bias may impact their objectivity.