10 citations · 11 across the 8 of their papers we have counts for
8 papers
Backdoor Attacks on Prompt-Driven Video Segmentation Foundation Models
Zongmin Zhang, Zhen Sun, Yifan Liao +5
Prompt-driven Video Segmentation Foundation Models (VSFMs), such as SAM2, are increasingly used in applications including autonomous driving and digital pathology, yet their securi…
Work Zones challenge VLM Trajectory Planning: Toward Mitigation and Robust Autonomous Driving
Yifan Liao, Zhen Sun, Xiaoyun Qiu +7
Visual Language Models (VLMs), with powerful multimodal reasoning capabilities, are gradually integrated into autonomous driving by several automobile manufacturers to enhance plan…
ZPD-SCA: Unveiling the Blind Spots of LLMs in Assessing Students' Cognitive Abilities
Wenhan Dong, Zhen Sun, Yuemeng Zhao +9
Large language models (LLMs) have demonstrated potential in educational applications, yet their capacity to accurately assess the cognitive alignment of reading materials with stud…
Evaluation Hallucination in Multi-Round Incomplete Information Lateral-Driven Reasoning Tasks
Wenhan Dong, Tianyi Hu, Jingyi Zheng +5
Multi-round incomplete information tasks are crucial for evaluating the lateral thinking capabilities of large language models (LLMs). Currently, research primarily relies on multi…
JALMBench: Benchmarking Jailbreak Vulnerabilities in Audio Language Models
Zifan Peng, Yule Liu, Zhen Sun +9
Large Audio Language Models (LALMs) have made significant progress. While increasingly deployed in real-world applications, LALMs face growing safety risks from jailbreak attacks t…
Humanizing LLMs: A Survey of Psychological Measurements with Tools, Datasets, and Human-Agent Applications
Wenhan Dong, Yuemeng Zhao, Zhen Sun +10
As large language models (LLMs) are increasingly used in human-centered tasks, assessing their psychological traits is crucial for understanding their social impact and ensuring tr…
Quantized Delta Weight Is Safety Keeper
Yule Liu, Zhen Sun, Xinlei He +1
Recent advancements in fine-tuning proprietary language models enable customized applications across various domains but also introduce two major challenges: high resource demands…
PEFTGuard: Detecting Backdoor Attacks Against Parameter-Efficient Fine-Tuning
Zhen Sun, Tianshuo Cong, Yule Liu +5
Fine-tuning is an essential process to improve the performance of Large Language Models (LLMs) in specific domains, with Parameter-Efficient Fine-Tuning (PEFT) gaining popularity d…