arXiv:2608.29943v1 Announce Type: new
Abstract: Large language models (LLMs) can memorize sensitive information, raising serious privacy concerns. Machine unlearning offers a potential solution to re...
By Shicheng Hu, Runzhi Tian, Ziqiao Wang, Yongyi Mao
The paper introduces Sparse One-hot Discrete Adam (SODA), a white‑box search algorithm that can reconstruct textual inputs from partial knowledge of a language model’s output logits. SODA can recover natural sequences of up to 17 tokens with no false positives, outperforming prior methods by a factor of four in exact‑match reconstruction. The authors demonstrate that input length and vocabulary size are more critical to inversion difficulty than model size, and provide proof‑of‑concept security applications, including recovering backdoor triggers and personally identifiable information from a poisoned LLM and synthetic user‑chatbot interactions.
By Adrians Skapars, Edoardo Manino, Youcheng Sun, Lucas C. Cordeiro
The paper introduces Dual-Embedding Watermarking (DEW), a semantic watermarking technique for large language models that combines contextual and token-level embeddings. DEW applies algebraic vector-space operations to generate a watermark signal that remains robust to paraphrasing and translation, while obfuscating the signal with pseudo-random matrices seeded by a secret key. Experiments demonstrate state‑of‑the‑art robustness, especially against translation, with minimal computational overhead and preserved text quality at lower watermark strengths.
By Jonas Sch\"afer, Cezary Pilaszewicz, Gerhard Wunder
arXiv:2508.03829v3 Announce Type: replace
Abstract: The growing deployment of Large Language Models (LLMs) has raised concerns about their misuse in generating harmful or deceptive content. To addres...
By Jiahao Xu, Rui Hu, Olivera Kotevska, Zikai Zhang
arXiv:2610.31995v1 Announce Type: cross
Abstract: As large language models (LLMs) face growing demands for data removal driven by regulatory compliance, copyright concerns, and privacy protection, ap...
By Mengying Zhang, Derui Wang, Ibrahim Khalil, Bowen Liu, Xiaoyu Xia, Minhui Xue
arXiv:2605.12890v2 Announce Type: replace-cross
Abstract: The rapid advancement of large language models (LLMs) has made machine-generated text increasingly difficult to distinguish from human-writte...
By Luxu Liang, Xiang Li