arXiv:2606. 15396v1 Announce Type: cross Abstract: Malicious content generated from large language models (LLMs) could pose severe safety risks and ethical concerns.
By Wenbo Yu, Bohua Wang, Hao Fang, Kuofeng Gao, Jingru Zeng, Xiaochen Yang, Tianyi Zhang, Xiaoxiao Ma, Jiawei Kong, Hao Wu, Bin Chen, Shu-Tao Xia, Min Zhang
arXiv:2608. 15600v1 Announce Type: new Abstract: The widespread circulation of abusive online content has increased the need for reliable moderation of Chinese social-media text.
By Mingyu Yuan, Shengtao Wen, Lingbing Guo, Zhen Bi, Xiang Chen
arXiv:2607. 15267v1 Announce Type: new Abstract: Poisoning pretraining data can introduce harmful behaviors to LMs that are difficult to detect and mitigate.
By Victoria Graf, Hannaneh Hajishirzi, Noah A. Smith, David Kohlbrenner, Kyle Lo
arXiv:2604. 13899v3 Announce Type: replace-cross Abstract: Instruction-tuned LLMs can annotate thousands of instances at low cost.
By Ahmad Dawar Hakimi, Lea Hirlimann, Isabelle Augenstein, Hinrich Sch\"utze
arXiv:2607. 03680v1 Announce Type: new Abstract: Recent AI-generated text detection work often introduces a new benchmark together with a specialized detector tailored to it.
By Zhuoer Shen, Mingyi Wang, Shaofeng Zou, Yuheng Bu
arXiv:2607. 14957v1 Announce Type: new Abstract: Online firestorms are rapid collective escalations of highly negative user-generated content and may cause substantial reputational and economic damage.
By Besim Shala, Peter Mandl, Andreas Humpe, Martin H\"ausl