6 papers
Mitigating Hallucinations in Large Language Models via Causal Reasoning
Yuangang Li, Yiqing Shen, Yi Nian +7
Large language models (LLMs) exhibit logically inconsistent hallucinations that appear coherent yet violate reasoning principles, with recent research suggesting an inverse relatio…
JailDAM: Jailbreak Detection with Adaptive Memory for Vision-Language Model
Yi Nian, Shenzhe Zhu, Yuehan Qin +4
Multimodal large language models (MLLMs) excel in vision-language tasks but also pose significant risks of generating harmful content, particularly through jailbreak attacks. Jailb…
Secure On-Device Video OOD Detection Without Backpropagation
Shawn Li, Peilin Cai, Yuxiao Zhou +7
Out-of-Distribution (OOD) detection is critical for ensuring the reliability of machine learning models in safety-critical applications such as autonomous driving and medical diagn…
AD-LLM: Benchmarking Large Language Models for Anomaly Detection
Tiankai Yang, Yi Nian, Shawn Li +9
Anomaly detection (AD) is an important machine learning task with many real-world uses, including fraud detection, medical diagnosis, and industrial monitoring. Within natural lang…
NLP-ADBench: NLP Anomaly Detection Benchmark
Yuangang Li, Jiaqi Li, Zhuo Xiao +4
Anomaly detection (AD) is an important machine learning task with applications in fraud detection, content moderation, and user behavior analysis. However, AD is relatively underst…
CMOOD: Concept-based Multi-label OOD Detection
Zhendong Liu, Yi Nian, Yuehan Qin +4
How can models effectively detect out-of-distribution (OOD) samples in complex, multi-label settings without extensive retraining? Existing OOD detection methods struggle to captur…