1 citations · 1 across the 7 of their papers we have counts for
9 papers
Absorber LLM: Harnessing Causal Synchronization for Test-Time Training
Zhixin Zhang, Shabo Zhang, Chengcan Wu +2
Transformers suffer from a high computational cost that grows with sequence length for self-attention, making inference in long streams prohibited by memory consumption. Constant-m…
The Salami Slicing Threat: Exploiting Cumulative Risks in LLM Systems
Yihao Zhang, Kai Wang, Jiangrong Wu +7
Large Language Models (LLMs) face prominent security risks from jailbreaking, a practice that manipulates models to bypass built-in security constraints and generate unethical or u…
Automata-Based Steering of Large Language Models for Diverse Structured Generation
Xiaokun Luan, Zeming Wei, Yihao Zhang +1
Large language models (LLMs) are increasingly tasked with generating structured outputs. While structured generation methods ensure validity, they often lack output diversity, a cr…
Dynamic Orthogonal Continual Fine-tuning for Mitigating Catastrophic Forgettings
Zhixin Zhang, Zeming Wei, Meng Sun
Catastrophic forgetting remains a critical challenge in continual learning for large language models (LLMs), where models struggle to retain performance on historical tasks when fi…
Reliable Unlearning Harmful Information in LLMs with Metamorphosis Representation Projection
Chengcan Wu, Zeming Wei, Huanran Chen +2
While Large Language Models (LLMs) have demonstrated impressive performance in various domains and tasks, concerns about their safety are becoming increasingly severe. In particula…
When Thinking LLMs Lie: Unveiling the Strategic Deception in Representations of Reasoning Models
Kai Wang, Yihao Zhang, Meng Sun
The honesty of large language models (LLMs) is a critical alignment challenge, especially as advanced systems with chain-of-thought (CoT) reasoning may strategically deceive humans…