most citedInterpreting and Improving Large Language Models in Arithmetic Calculation

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

collaborators

5 papers

cs.CL2025

Advancing Machine-Generated Text Detection from an Easy to Hard Supervision Perspective

Chenwang Wu, Yiu-ming Cheung, Bo Han +1

Existing machine-generated text (MGT) detection methods implicitly assume labels as the "golden standard". However, we reveal boundary ambiguity in MGT detection, implying that tra…

cs.LG2025

FedGPS: Statistical Rectification Against Data Heterogeneity in Federated Learning

Zhiqin Yang, Yonggang Zhang, Chenxin Li +3

Federated Learning (FL) confronts a significant challenge known as data heterogeneity, which impairs model performance and convergence. Existing methods have made notable progress…

cs.CV2025

Semantic-guided Fine-tuning of Foundation Model for Long-tailed Visual Recognition

Yufei Peng, Yonggang Zhang, Yiu-ming Cheung

The variance in class-wise sample sizes within long-tailed scenarios often results in degraded performance in less frequent classes. Fortunately, foundation models, pre-trained on…

cs.CV2024

Epistemic Uncertainty for Generated Image Detection

Jun Nie, Yonggang Zhang, Tongliang Liu +3

We introduce a novel framework for AI-generated image detection through epistemic uncertainty, aiming to address critical security concerns in the era of generative models. Our key…

cs.CL20241 cited

Interpreting and Improving Large Language Models in Arithmetic Calculation

Wei Zhang, Chaoqun Wan, Yonggang Zhang +4

Large language models (LLMs) have demonstrated remarkable potential across numerous applications and have shown an emergent ability to tackle complex reasoning tasks, such as mathe…