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Zhengwu Zhang

16 papers hereh-index 11764 citations26 works total

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

author position
  • first author8
  • middle author7

Across the 15 of 16 papers where every author was matched, so the position is known.

fields
  • cs.LG10
  • cs.CL4
  • cs.IR1
  • cs.SI1
same name
  • Zhengwu Zhang — 36 papers, h 21
  • Zhengwu Zhang — 1 paper, h 2
  • Zhengwu Zhang — 1 paper, h 0
  • Zhengwu Zhang — 1 paper, h 1
  • Zhengwu Zhang — 1 paper, 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
20232025
most citedBalancing Specialized and General Skills in LLMs: The Impact of Modern Tuning and Data Strategy

12 citations · 30 across the 16 of their papers we have counts for

collaborators
Showing cs.CLShow all

4 papers · 1 filter

cs.CL2024

Taxonomy Tree Generation from Citation Graph

Yuntong Hu, Zhuofeng Li, Zheng Zhang +4

Constructing taxonomies from citation graphs is essential for organizing scientific knowledge, facilitating literature reviews, and identifying emerging research trends. However, m…

cs.CL2024★ 2 cited

TEG-DB: A Comprehensive Dataset and Benchmark of Textual-Edge Graphs

Zhuofeng Li, Zixing Gou, Xiangnan Zhang +6

Text-Attributed Graphs (TAGs) augment graph structures with natural language descriptions, facilitating detailed depictions of data and their interconnections across various real-w…

cs.CL2024

Distilling Large Language Models for Text-Attributed Graph Learning

Bo Pan, Zheng Zhang, Yifei Zhang +2

Text-Attributed Graphs (TAGs) are graphs of connected textual documents. Graph models can efficiently learn TAGs, but their training heavily relies on human-annotated labels, which…

cs.CL2023★ 12 cited

Balancing Specialized and General Skills in LLMs: The Impact of Modern Tuning and Data Strategy

Zheng Zhang, Chen Zheng, Da Tang +5

This paper introduces a multifaceted methodology for fine-tuning and evaluating large language models (LLMs) for specialized monetization tasks. The goal is to balance general lang…

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