2 citations · 4 across the 10 of their papers we have counts for
4 papers · 1 filter
Decoupling Safety into Orthogonal Subspace: Cost-Efficient and Performance-Preserving Alignment for Large Language Models
Yutao Mou, Xiaoling Zhou, Yuxiao Luo +2
Safety alignment is essential for building trustworthy artificial intelligence, yet it remains challenging to enhance model safety without degrading general performance. Current ap…
AutoRed: A Free-form Adversarial Prompt Generation Framework for Automated Red Teaming
Muxi Diao, Yutao Mou, Keqing He +6
The safety of Large Language Models (LLMs) is crucial for the development of trustworthy AI applications. Existing red teaming methods often rely on seed instructions, which limits…
Can You Really Trust Code Copilots? Evaluating Large Language Models from a Code Security Perspective
Yutao Mou, Xiao Deng, Yuxiao Luo +2
Code security and usability are both essential for various coding assistant applications driven by large language models (LLMs). Current code security benchmarks focus solely on si…
SaRO: Enhancing LLM Safety through Reasoning-based Alignment
Yutao Mou, Yuxiao Luo, Shikun Zhang +1
Current safety alignment techniques for large language models (LLMs) face two key challenges: (1) under-generalization, which leaves models vulnerable to novel jailbreak attacks, a…