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Qiang Fu

4 papers hereh-index 4203 citations6 works total

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

author position
  • middle author4

Across the 4 of 4 papers where every author was matched, so the position is known.

fields
  • cs.LG2
  • cs.AI1
  • cs.CL1
same name
  • Qiang Fu — 21 papers, h 13
  • Qiang Fu — 21 papers, h 19
  • Qiang Fu — 13 papers, h 8
  • Qiang Fu — 12 papers, h 25
  • Qiang Fu — 10 papers, h 8
  • Qiang Fu — 7 papers, h 15

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

most citedSource Free Unsupervised Graph Domain Adaptation

14 citations · 17 across the 4 of their papers we have counts for

collaborators

4 papers

cs.LG2024

Hadamard Adapter: An Extreme Parameter-Efficient Adapter Tuning Method for Pre-trained Language Models

Yuyan Chen, Qiang Fu, Ge Fan +6

Recent years, Pre-trained Language models (PLMs) have swept into various fields of artificial intelligence and achieved great success. However, most PLMs, such as T5 and GPT3, have…

cs.CL2024

Hallucination Detection: Robustly Discerning Reliable Answers in Large Language Models

Yuyan Chen, Qiang Fu, Yichen Yuan +6

Large Language Models (LLMs) have gained widespread adoption in various natural language processing tasks, including question answering and dialogue systems. However, a major drawb…

cs.AI2024★ 3 cited

An Examination on the Effectiveness of Divide-and-Conquer Prompting in Large Language Models

Yizhou Zhang, Lun Du, Defu Cao +2

Foundation models, such as Large language Models (LLMs), have attracted significant amount of interest due to their large number of applications. However, when handling tasks invol…

cs.LG2021★ 14 cited

Source Free Unsupervised Graph Domain Adaptation

Haitao Mao, Lun Du, Yujia Zheng +5

Graph Neural Networks (GNNs) have achieved great success on a variety of tasks with graph-structural data, among which node classification is an essential one. Unsupervised Graph D…

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