7 papers
Can Large Language Models Express Uncertainty Like Human?
Linwei Tao, Yi-Fan Yeh, Bo Kai +6
Large language models (LLMs) are increasingly used in high-stakes settings, where overconfident responses can mislead users. Reliable confidence estimation has been shown to enhanc…
WATS: Calibrating Graph Neural Networks with Wavelet-Aware Temperature Scaling
Xiaoyang Li, Linwei Tao, Haohui Lu +3
Graph Neural Networks (GNNs) have demonstrated strong predictive performance on relational data; however, their confidence estimates often misalign with actual predictive correctne…
Revisiting Uncertainty Estimation and Calibration of Large Language Models
Linwei Tao, Yi-Fan Yeh, Minjing Dong +3
As large language models (LLMs) are increasingly deployed in high-stakes applications, robust uncertainty estimation is essential for ensuring the safe and trustworthy deployment o…
Beyond One-Hot Labels: Semantic Mixing for Model Calibration
Haoyang Luo, Linwei Tao, Minjing Dong +1
Model calibration seeks to ensure that models produce confidence scores that accurately reflect the true likelihood of their predictions being correct. However, existing calibratio…
VEAttack: Downstream-agnostic Vision Encoder Attack against Large Vision Language Models
Hefei Mei, Zirui Wang, Shen You +2
Large Vision-Language Models (LVLMs) have demonstrated remarkable capabilities in multimodal understanding and generation, yet their vulnerability to adversarial attacks raises sig…
Uncertainty Weighted Gradients for Model Calibration
Jinxu Lin, Linwei Tao, Minjing Dong +1
Model calibration is essential for ensuring that the predictions of deep neural networks accurately reflect true probabilities in real-world classification tasks. However, deep net…