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20232026
most citedA Benchmark Study on Calibration

1 citations · 1 across the 15 of their papers we have counts for

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Showing 2025Show all

7 papers · 1 filter

cs.CL2025

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…

cs.LG2025

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…

cs.LG2025

Sample Margin-Aware Recalibration of Temperature Scaling

Haolan Guo, Linwei Tao, Haoyang Luo +2

Recent advances in deep learning have significantly improved predictive accuracy. However, modern neural networks remain systematically overconfident, posing risks for deployment i…

cs.CL2025

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…

cs.CV2025

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…

cs.LG2025

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…

cs.CV2025

Mitigating Object Hallucinations in Large Vision-Language Models via Attention Calibration

Younan Zhu, Linwei Tao, Minjing Dong +1

Large Vision-Language Models (LVLMs) exhibit impressive multimodal reasoning capabilities but remain highly susceptible to object hallucination, where models generate responses tha…