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Sparse-BitNet: 1.58-bit LLMs are Naturally Friendly to Semi-Structured Sparsity
Large Language Models report from Microsoft with 12 connected researchers in the LLMpeople atlas.
Connected researchers
Researcher
1 reports
Xun Wu
Microsoft
Xun Wu is listed as a coauthor of the arXiv paper "Sparse-BitNet: 1.58-bit LLMs are Naturally Friendly to Semi-Structured Sparsity," with affiliation 1 shown as Microsoft Research.
Researcher
6 reports
Shaohan Huang
Microsoft
Shaohan Huang is a senior researcher in the General Artificial Intelligence Group at Microsoft Research Asia in Beijing. OpenReview lists him as a Microsoft researcher and a former master's student at Beihang University.
Researcher
1 reports
Yingbo Hao
Microsoft
Public Microsoft-linked sources identify Yingbo Hao as a coauthor on recent efficient language-model technical reports.
Researcher
2 reports
Zewen Chi
Microsoft
Zewen Chi is listed as a co-author of the 2026 arXiv paper "Sparse-BitNet: 1.58-bit LLMs are Naturally Friendly to Semi-Structured Sparsity," with affiliation 1 shown as Microsoft Research.
Researcher
5 reports
Li Dong
Microsoft
Li Dong is a Microsoft Research principal researcher focused on human language technologies and machine intelligence.
Researcher
2 reports
Ting Song
Microsoft
Ting Song is listed as an author of the BitNet b1.58 2B4T Technical Report; the report states that T. Song is with Microsoft Research.
Researcher
2 reports
Yan Xia
Microsoft
Co-author of the BitNet b1.58 2B4T Technical Report; the report states Yan Xia is with Microsoft Research.
Researcher
1 reports
Zhifang Sui
Microsoft
Public Peking University faculty pages list Zhifang Sui as a professor in the Institute of Computational Linguistics with research interests in natural language processing and computational linguistics.