Automated fact-checking is a needed technology to curtail the spread of online misinformation. One current framework for such solutions proposes to verify claims by retrieving supporting or refuting evidence from related textual sources. However, the realistic use cases for fact-checkers will require verifying claims against evidence sources that could be affected by the same misinformation. Furthermore, the development of modern NLP tools that can produce coherent, fabricated content would allow malicious actors to systematically generate adversarial disinformation for fact-checkers.
In this work, we explore the sensitivity of automated fact-checkers to synthetic adversarial evidence in two simulated settings: AdversarialAddition, where we fabricate documents and add them to the evidence repository available to the fact-checking system, and AdversarialModification, where existing evidence source documents in the repository are automatically altered. Our study across multiple models on three benchmarks demonstrates that these systems suffer significant performance drops against these attacks. Finally, we discuss the growing threat of modern NLG systems as generators of disinformation in the context of the challenges they pose to automated fact-checkers.
ClimateCheck 2026: Scientific Fact-Checking and Disinformation Narrative Classification of Climate-related Claims
Deutsches Forschungszentrum für Künstliche Intelligenz GmbH (DFKI)（德国人工智能研究中心）；Technische Universität Berlin（柏林工业大学）；Leuphana Universität Lüneburg（吕讷堡大学）；Humboldt-Universität zu Berlin（柏林洪堡大学）
BiDeV: Bilateral Defusing Verification for Complex Claim Fact-Checking
Gaoling School of Artificial Intelligence, Renmin University of China（中国人民大学高瓴人工智能学院）；Nankai University（南开大学）；Baidu Inc.（百度公司）；Kunming University of Science and Technology（昆明理工大学）
PASTA: Table-Operations Aware Fact Verification via Sentence-Table Cloze Pre-training
Renmin University of China（中国人民大学）；Qatar Computing Research Institute（卡塔尔计算研究所）；Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）
事实验证最近吸引了大量的研究关注，例如，在新闻业、市场营销和政策制定方面，网上的错误信息和虚假信息可以动摇一个人的观点，影响一个人的行动。虽然事实核查通常是一项艰巨的任务，但在许多情况下，虚假陈述可以很容易地根据对具有可靠信息的表格的分析来揭穿。因此，基于表格的事实验证最近已经成为一个重要的和不断增长的研究领域。然而，由于缺乏可用于预训练语言模型（LM）以了解常见表操作（例如聚合列或比较元组）的数据集，因此进展有限。为了弥补这一差距，在本文中，我们介绍了PASTA，一个新的国家的最先进的框架，基于表格的事实验证通过预训练与合成的表格完形填空题。特别是，我们设计了六种类型的常见的表完形填空任务，包括过滤，聚合，最高级，比较，顺序，和唯一的，基于此，我们合成了一个大型语料库，由120万个表对从WikiTables。PASTA使用最近预训练的LM DeBERTaV 3，并在我们的语料库上进一步预训练它。我们的实验结果表明，PASTA实现了新的国家的最先进的性能基于表的事实验证基准：TabFact和SEM-TAB-FACTS。特别是，在包含多个操作的TabFact复杂集上，PASTA的表现大大优于先前的最新技术水平4.7分（85.6% vs. 80.9%），并且PASTA和人类在小型TabFact测试集上的表现之间的差距缩小到仅1.5分（90.6% vs. 92.1%）。
