5 papers
CASE-Bench: Context-Aware SafEty Benchmark for Large Language Models
Guangzhi Sun, Xiao Zhan, Shutong Feng +2
Aligning large language models (LLMs) with human values is essential for their safe deployment and widespread adoption. Current LLM safety benchmarks often focus solely on the refu…
Uncertainty-based Debiasing and Unlearning for Decontamination
Guangzhi Sun, Xiao Zhan, Mark Gales
Benchmark-based evaluation is the dominant paradigm for assessing large language model (LLM) capabilities, yet data contamination inflates reported performance and undermines fair…
Protecting Bystander Privacy via Selective Hearing in Audio LLMs
Xiao Zhan, Guangzhi Sun, Jose Such +1
Audio Large language models (LLMs) are increasingly deployed in the real world, where they inevitably capture speech from unintended nearby bystanders, raising privacy risks that e…
Unlearning vs. Obfuscation: Are We Truly Removing Knowledge?
Guangzhi Sun, Potsawee Manakul, Xiao Zhan +1
Unlearning has emerged as a critical capability for large language models (LLMs) to support data privacy, regulatory compliance, and ethical AI deployment. Recent techniques often…
Can Multi-modal (reasoning) LLMs work as deepfake detectors?
Simiao Ren, Yao Yao, Kidus Zewde +8
Deepfake detection remains a critical challenge in the era of advanced generative models, particularly as synthetic media becomes more sophisticated. In this study, we explore the…