1 citations · 2 across the 10 of their papers we have counts for
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RMCW: A Deletion-Robust Watermark Based on Reed--Muller Codes for Language Models
Yi Wang, Baicheng Chen, Yu Wang +3
Large Language Model (LLM) watermarking provides a lightweight mechanism for identifying text generated by a specific model, but its robustness remains fragile under post-processin…
Defending Against Malicious Finetuning by Scaling Train-time Adversarial Attacks
Haoming Wen, Shi Chen, Qingyu Shi +4
Malicious finetuning attacks pose a major safety threat against open-weight large language models (LLMs). However, existing alignment-stage defenses provide limited protection agai…
Prioritization of Risks from Artificial Intelligence: A Delphi Study of 272 International Experts
Alexander K. Saeri, Jess Graham, Michael Noetel +185
Artificial intelligence poses many risks, ranging from familiar present-day harms to unprecedented and potentially catastrophic ones. Effective risk management requires prioritizat…
Can Large Language Models Reinvent Foundational Algorithms?
Jian Zhao, Haoren Luo, Yu Wang +3
LLMs have shown strong potential to advance scientific discovery. Whether they possess the capacity for foundational innovation, however, remains an open question. In this work, we…
CREBench: Evaluating Large Language Models in Cryptographic Binary Reverse Engineering
Baicheng Chen, Yu Wang, Ziheng Zhou +4
Reverse engineering (RE) is central to software security, particularly for cryptographic programs that handle sensitive data and are highly prone to vulnerabilities. It supports cr…
Reverse-Engineering Model Editing on Language Models
Zhiyu Sun, Minrui Luo, Yu Wang +2
Large language models (LLMs) are pretrained on corpora containing trillions of tokens and, therefore, inevitably memorize sensitive information. Locate-then-edit methods, as a main…