arXiv AI By Maoyuan Li, Haoyuan Li, Zhongsheng Wang, Jiamou Liu

Value-Rich, Differently Organized: Measuring Value Expression and Organization in AI-Generated Debates

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arXiv AI
Aug 20

A Theory of Post-hoc Debate Judgement

The paper proposes a theory for judging post-hoc debates in AI, focusing on properties like reproducibility, robustness, groundedness, and explainability. It evaluates two debate‑judgement methods—LLM judges and formal computational argumentation semantics—finding similar accuracy but noting that argumentation semantics offers stronger formal guarantees. The study suggests that argumentation semantics is a preferable framework for principled debate judges in AI systems.

By Xiang Yin, Adam Dejl, Antonio Rago, Lihu Chen, Francesca Toni
arXiv Computation and Language
Sep 1

Evaluating the Capabilities of LLMs for Persuasive Dialogue

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.

By Jordan Robinson, Angus R. Williams, Katie Atkinson, Anthony G. Cohn
arXiv Computation and Language
Sep 10

Who Argues What? Joint Argument-Entity Detection and Classification in Political Debates

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%).

By Lucio La Cava, Stefano Francesco Monea, Sergio Greco