activity
20222024
most citedSuperCLUE: A Comprehensive Chinese Large Language Model Benchmark

25 citations · 42 across the 7 of their papers we have counts for

collaborators

7 papers

cs.CL20242 cited

SuperCLUE-Math6: Graded Multi-Step Math Reasoning Benchmark for LLMs in Chinese

Liang Xu, Hang Xue, Lei Zhu +1

We introduce SuperCLUE-Math6(SC-Math6), a new benchmark dataset to evaluate the mathematical reasoning abilities of Chinese language models. SC-Math6 is designed as an upgraded Chi…

cs.LG20236 cited

Rethinking Client Drift in Federated Learning: A Logit Perspective

Yunlu Yan, Chun-Mei Feng, Mang Ye +5

Federated Learning (FL) enables multiple clients to collaboratively learn in a distributed way, allowing for privacy protection. However, the real-world non-IID data will lead to c…

cs.CL202325 cited

SuperCLUE: A Comprehensive Chinese Large Language Model Benchmark

Liang Xu, Anqi Li, Lei Zhu +7

Large language models (LLMs) have shown the potential to be integrated into human daily lives. Therefore, user preference is the most critical criterion for assessing LLMs' perform…

cs.CV20231 cited

Distribution Aligned Diffusion and Prototype-guided network for Unsupervised Domain Adaptive Segmentation

Haipeng Zhou, Lei Zhu, Yuyin Zhou

The Diffusion Probabilistic Model (DPM) has emerged as a highly effective generative model in the field of computer vision. Its intermediate latent vectors offer rich semantic info…

cs.LG20233 cited

A Comprehensive Survey on Source-free Domain Adaptation

Zhiqi Yu, Jingjing Li, Zhekai Du +2

Over the past decade, domain adaptation has become a widely studied branch of transfer learning that aims to improve performance on target domains by leveraging knowledge from the…

eess.IV20225 cited

NestedFormer: Nested Modality-Aware Transformer for Brain Tumor Segmentation

Zhaohu Xing, Lequan Yu, Liang Wan +2

Multi-modal MR imaging is routinely used in clinical practice to diagnose and investigate brain tumors by providing rich complementary information. Previous multi-modal MRI segment…