The paper investigates how well large language models (LLMs) can handle character attacks—ad hominem arguments—in political debates. By analyzing natural political dialogues and comparing LLM-generated responses to a corpus of U.S. presidential debates, the study finds that most LLMs favor logical defenses and rarely use ethos-based counterattacks. The authors suggest that safety fine‑tuning limits LLMs’ strategic options, preventing them from fully engaging in realistic political discourse.
By Ewelina Gajewska, Katarzyna Budzynska, Jaroslaw Chudziak
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.
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...
By Martial Pastor, Nelleke Oostdijk
ARGUS is a new agent-based framework for persuasive argument generation that incorporates a Theory-of-Mind Reasoner to model audience beliefs and values. It uses a component-aware planner to break arguments into subtopics, assign rhetorical functions (logos, pathos, ethos, kairos), and guide evidence retrieval during planning. A refinement module iteratively addresses multi-dimensional weaknesses, and evaluations on three benchmarks show ARGUS outperforming strong baselines and effectively shifting resistant audience stances.
By Zhe Hu
arXiv:2610.20548v1 Announce Type: new
Abstract: Understanding the social intelligence of large language models (LLMs) is increasingly important for applications involving human-AI interaction and mul...
By Ali Zamani, Soroush Esfahanian, Erfan Shahabi, Mohammad Taher Pilehvar, Azadeh Shakery, Yadollah Yaghoobzadeh
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.
By Allen Roush, Yusuf Shabazz, Arvind Balaji, Peter Zhang, Stefano Mezza, Markus Zhang, Sanjay Basu, Sriram Vishwanath, Mehdi Fatemi, Ravid Shwartz-Ziv