Zirui Hu

Large language models (LLMs) have achieved remarkable performance across diverse tasks, largely driven by large-scale pretraining. However, this data abundance introduces test data contamination, where benchmark datasets overlap with pretraining corpora, undermining the reliability of model evalu...

Large language models (LLMs) have achieved remarkable performance across a wide range of natural language processing (NLP) tasks. However, they remain susceptible to backdoor attacks, where adversaries embed hidden triggers in the input to induce malicious, attacker-specified behaviors. While exi...