arXiv:2607. 20730v1 Announce Type: cross Abstract: Large language models increasingly use search tools to retrieve up-to-date information, introducing a new attack surface in which retrieved documents can be manipulated.
By Zhaoqi Wang, Zijian Zhang, Xiaomei Yuan, Pengtao Kou, Jiamou Liu, Zhen Li, Liehuang Zhu
arXiv:2607. 17935v1 Announce Type: cross Abstract: Automated fact-checking remains a challenge for Large Language Models (LLMs) due to "query brittleness" in traditional retrieval systems.
By Cong Hoan Nguyen, Thomas Hoang, Hieu Minh Duong, Long Nguyen
arXiv:2501. 14728v2 Announce Type: replace-cross Abstract: While generative artificial intelligence (GenAI) models have achieved significant success, their misuse for generating deceptive content raises growing concerns about online information security.
By Zehong Yan, Peng Qi, Wynne Hsu, Mong Li Lee
The paper introduces Fact-Ablated Evaluation (FAE), a framework that iteratively removes cited evidence to test whether large language models (LLMs) adjust their fact‑checking predictions accordingly. Experiments reveal that many off‑the‑shelf LLMs rely more on internal knowledge than on the provided evidence. To address this, the authors propose REAL, a training method that uses counterfactual evidence supervision to encourage LLMs to base veracity judgments on evidence, achieving better evidence‑dependent performance across four datasets.
By Xingyu Deng, Mingzi Cao, Nikolaos Aletras, Xi Wang, Mark Stevenson
arXiv:2606. 13262v1 Announce Type: new Abstract: Recent approaches combining Large Language Models (LLMs) with retrieval-augmented reasoning have shown promise for automated fact verification.
By Rongxin Yang, Shenghong He, Siyuan Zhu, Chao Yu
The paper introduces a new type of adversarial attack on automated fact‑checking systems that uses large language models to rephrase claims with persuasive techniques. By applying 15 persuasion methods across five categories, the authors evaluate how these rewrites affect claim verification and evidence retrieval on the FEVER and FEVEROUS benchmarks. Results show that persuasive rewrites significantly degrade both verification accuracy and evidence retrieval performance, underscoring the vulnerability of current fact‑checking systems to such attacks.
By Jo\~ao A. Leite, Olesya Razuvayevskaya, Kalina Bontcheva, Carolina Scarton