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Weiping Wang

28 papers hereh-index 14957 citations71 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • middle author1
  • last author23

Across the 24 of 28 papers where every author was matched, so the position is known.

fields
  • cs.LG21
  • cs.CL4
  • cs.CV2
  • cs.CR1
same name
  • Weiping Wang — 36 papers, h 21
  • Weiping Wang — 31 papers, h 24
  • Weiping Wang — 22 papers, h 6
  • Weiping Wang — 6 papers, h 16
  • Weiping Wang — 6 papers, h 6
  • Weiping Wang — 5 papers, h 2

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20192025
most citedA Survey on Model Compression for Large Language Models

37 citations · 97 across the 26 of their papers we have counts for

collaborators
Showing cs.CLShow all

4 papers · 1 filter

cs.CL2025

SSTAG: Structure-Aware Self-Supervised Learning Method for Text-Attributed Graphs

Ruyue Liu, Rong Yin, Xiangzhen Bo +5

Large scale pretrained models have revolutionized Natural Language Processing (NLP) and Computer Vision (CV), showcasing remarkable cross domain generalization abilities. However,…

cs.CL2024★ 1 cited

Key-Point-Driven Mathematical Reasoning Distillation of Large Language Model

Xunyu Zhu, Jian Li, Can Ma +1

Large Language Models (LLMs) have demonstrated exceptional proficiency in mathematical reasoning tasks due to their extensive parameter counts and training on vast datasets. Despit…

cs.CL2024

Distilling Mathematical Reasoning Capabilities into Small Language Models

Xunyu Zhu, Jian Li, Yong Liu +2

This work addresses the challenge of democratizing advanced Large Language Models (LLMs) by compressing their mathematical reasoning capabilities into sub-billion parameter Small L…

cs.CL2023★ 37 cited

A Survey on Model Compression for Large Language Models

Xunyu Zhu, Jian Li, Yong Liu +2

Large Language Models (LLMs) have transformed natural language processing tasks successfully. Yet, their large size and high computational needs pose challenges for practical use,…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.