When engaging in argumentative discourse, skilled human debaters tailor claims to the beliefs of the audience, to construct effective arguments. Recently, the field of computational argumentation witnessed extensive effort to address the automatic generation of arguments. However, existing approaches do not perform any audience-specific adaptation. In this work, we aim to bridge this gap by studying the task of belief-based claim generation: Given a controversial topic and a set of beliefs, generate an argumentative claim tailored to the beliefs. To tackle this task, we model the people's prior beliefs through their stances on controversial topics and extend state-of-the-art text generation models to generate claims conditioned on the beliefs. Our automatic evaluation confirms the ability of our approach to adapt claims to a set of given beliefs. In a manual study, we additionally evaluate the generated claims in terms of informativeness and their likelihood to be uttered by someone with a respective belief. Our results reveal the limitations of modeling users' beliefs based on their stances, but demonstrate the potential of encoding beliefs into argumentative texts, laying the ground for future exploration of audience reach.
End-to-End Argument Mining through Autoregressive Argumentative Structure Prediction
Applied Machine Learning (AMaL) Lab（应用机器学习（AMaL）实验室）；Department of Computer Science and Engineering, Indian Institute of Technology (IIT), Guwahati, India（计算机科学与工程系，印度理工学院（IIT），古瓦哈蒂，印度）；North Eastern Regional Institute of Science and Technology (NERIST), Arunachal Pradesh, India（北东部科学与技术研究所（NERIST），阿鲁纳恰尔邦，印度）
提出 AASP 自回归框架，以条件预训练语言模型和受约束动作逐步生成论证结构，端到端联合完成论证挖掘，并在三个标准基准上验证其 SoTA 性能。
CasiMedicos-Arg: A Medical Question Answering Dataset Annotated with Explanatory Argumentative Structures
Université Côte d’Azur（蔚蓝海岸大学）；CNRS（法国国家科学研究中心）；Inria（法国国家信息与自动化研究所）；I3S（信息科学与信号处理实验室）；HiTZ Center - Ixa（HiTZ中心-Ixa团队）；University of the Basque Country UPV/EHU（巴斯克大学）
Argument Rarity-based Originality Assessment for AI-Assisted Writing
Ritsumeikan Global Innovation Research Organization (R-GIRO), Ritsumeikan University（日光全球创新研究组织（R-GIRO）、日光大学）；Faculty of Business and Commerce, Kansai University（大阪大学商学部）；Arbege Corporation（Arbege公司）；College of Life Sciences, Ritsumeikan University（日光大学生命科学学院）；Office of General Education, Ritsumeikan University（日光大学通识教育办公室）
Universidade de São Paulo Center for Artificial Intelligence (C4AI)（圣保罗大学人工智能中心（C4AI））；Instituto Mauá de Tecnologia Núcleo de Sistemas Eletrônicos Embarcados (NSEE)（马乌阿技术研究所电子系统嵌入核（NSEE））
University of California, Los Angeles（加州大学洛杉矶分校）；Seoul National University（首尔国立大学）；Qatar Computing Research Institute（卡塔尔计算研究所）；Google（谷歌）；George Washington University（乔治华盛顿大学）；Stanford University（斯坦福大学）；Scale AI；University of Washington（华盛顿大学）
A comprehensive study of LLM-based argument classification: from Llama through DeepSeek to GPT-5.2
Faculty of Computer Science, Faculty of Humanities（计算机科学系，人文科学系）；Institute of Mathematics Faculty of Humanities（人文科学学院数学研究所）；University of Silesia（斯蒂利亚大学）；AGH University of Krakow（克拉科夫AGH大学）
Overview of AI-Debater 2023: The Challenges of Argument Generation Tasks
Fudan University（复旦大学）；Zhongyuan University of Technology（中原工学院）；Southeast University（东南大学）；Hainan University（海南大学）；South China Agricultural University（华南农业大学）
