arXiv:2606. 14060v1 Announce Type: new Abstract: Adversarial conditions such as paraphrasing and targeted style transfer sharply degrade the accuracy of machine text detectors.
By Aleem Khan, Nicholas Andrews
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:2608. 04052v1 Announce Type: cross Abstract: Backdoor attacks in multimodal contrastive learning (MCL) have garnered growing attention in recent years, as many downstream tasks critically depend on pre-trained MCL models.
By Yiming Chen, Kemou Li, Haiwei Wu, Jiantao Zhou
arXiv:2606. 04177v1 Announce Type: cross Abstract: Interpretable linguistic features offer a promising approach for explaining why a given text appears machine-generated, particularly for non-expert users.
By Yassir El Attar, Esra D\"onmez, Maximilian Maurer, Agnieszka Falenska
arXiv:2508. 09105v3 Announce Type: replace Abstract: Retrieval-Augmented Generation (RAG) and its Multimodal Retrieval-Augmented Generation (MRAG) significantly improve the knowledge coverage and contextual understanding of Large Language Models (LLMs) by introducing external knowledge sources.
By Shixuan Sun, Siyuan Liang, Jianjie Huang, Jingzhi Li, Xiaochun Cao
arXiv:2409. 10094v3 Announce Type: replace-cross Abstract: Out-of-Distribution (OoD) detection aims to justify whether a given sample is from the training distribution of the classifier-under-protection, i.
By Kun Fang, Zuopeng Yang, Haibo Hu, Xiaolin Huang, Jie Yang, Qinghua Tao