5 citations · 24 across the 44 of their papers we have counts for
5 papers · 1 filter
Can MLLMs Decode the Creative Leap? Introducing C4 for Cross-Concept Understanding
Ming Wang, Yuqing Zhang, Tingna Xie +5
Creative capabilities of MLLMs matter in design, communication, education, and human--AI collaboration, yet remain difficult to evaluate because explicit targets and reward signals…
From Parameter Dynamics to Risk Scoring : Quantifying Sample-Level Safety Degradation in LLM Fine-tuning
Xiao Wang, Yifei Zhang, YongKang Liu +4
Safety alignment of Large Language Models (LLMs) is extremely fragile, as fine-tuning on a small number of benign samples can erase safety behaviors learned from millions of prefer…
Defending Large Language Models Against Jailbreak Attacks via In-Decoding Safety-Awareness Probing
Yinzhi Zhao, Ming Wang, Shi Feng +3
Large language models (LLMs) have achieved impressive performance across natural language tasks and are increasingly deployed in real-world applications. Despite extensive safety a…
T-COL: Generating Counterfactual Explanations for General User Preferences on Variable Machine Learning Systems
Ming Wang, Daling Wang, Wenfang Wu +2
To address the interpretability challenge in machine learning (ML) systems, counterfactual explanations (CEs) have emerged as a promising solution. CEs are unique as they provide w…
Alleviating Sparsity of Open Knowledge Graphs with Ternary Contrastive Learning
Qian Li, Shafiq Joty, Daling Wang +2
Sparsity of formal knowledge and roughness of non-ontological construction make sparsity problem particularly prominent in Open Knowledge Graphs (OpenKGs). Due to sparse links, lea…