2 citations · 3 across the 14 of their papers we have counts for
7 papers · 1 filter
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…
Securing Multi-Agent Systems Against Corruptions via Node Contribution Backpropagation
Chengcan Wu, Zhixin Zhang, Mingqian Xu +2
Multi-Agent Systems (MAS) have become a prevalent paradigm for Large Language Model (LLM) applications. However, the complex multi-agent design in MAS introduces unique trustworthi…
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…
ReGA: Model-Based Safeguard for LLMs via Representation-Guided Abstraction
Zeming Wei, Chengcan Wu, Meng Sun
Large Language Models (LLMs) have achieved tremendous success in various tasks, yet concerns about their safety and security have emerged. In particular, they pose risks of generat…
Secure LLM Fine-Tuning via Safety-Aware Probing
Chengcan Wu, Zhixin Zhang, Zeming Wei +3
Large language models (LLMs) have achieved remarkable success across many applications, but their ability to generate harmful content raises serious safety concerns. Although safet…