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researcher

Fang Wu

4 papers hereh-index 326 citations8 works total

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

author position
  • first author1
  • middle author3

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

fields
  • cs.CL2
  • cs.LG2
same name
  • Fang Wu — 12 papers, h 4
  • Fang Wu — 11 papers, h 6
  • Fang Wu — 8 papers, h 4
  • Fang Wu — 6 papers, h 4
  • Fang Wu — 6 papers, h 3
  • Fang Wu — 3 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
20242026
most citedInsertGNN: Can Graph Neural Networks Outperform Humans in TOEFL Sentence Insertion Problem?

2 citations · 2 across the 1 of their papers we have counts for

collaborators

4 papers

cs.CL2026★ 2 cited

InsertGNN: Can Graph Neural Networks Outperform Humans in TOEFL Sentence Insertion Problem?

Fang Wu, Stan Z. Li

The integration of sentences poses an intriguing challenge within the realm of NLP, but it has not garnered the attention it deserves. Existing methods that focus on sentence arran…

cs.LG2025

AnchorAttention: Difference-Aware Sparse Attention with Stripe Granularity

Yu Zhang, Dong Guo, Fang Wu +3

Large Language Models (LLMs) with extended context lengths face significant computational challenges during the pre-filling phase, primarily due to the quadratic complexity of self…

cs.CL2025

FanChuan: A Multilingual and Graph-Structured Benchmark For Parody Detection and Analysis

Yilun Zheng, Sha Li, Fangkun Wu +9

Parody is an emerging phenomenon on social media, where individuals imitate a role or position opposite to their own, often for humor, provocation, or controversy. Detecting and an…

cs.LG2024

Evaluating the Generalization Ability of Quantized LLMs: Benchmark, Analysis, and Toolbox

Yijun Liu, Yuan Meng, Fang Wu +7

Large language models (LLMs) have exhibited exciting progress in multiple scenarios, while the huge computational demands hinder their deployments in lots of real-world application…

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