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Qiang Fu

12 papers hereh-index 253.3k citations36 works total

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

author position
  • first author1
  • middle author11

Across the 12 of 12 papers where every author was matched, so the position is known.

fields
  • cs.LG6
  • cs.CL5
  • cs.SI1
same name
  • Qiang Fu — 21 papers, h 13
  • Qiang Fu — 21 papers, h 19
  • Qiang Fu — 12 papers, h 8
  • Qiang Fu — 10 papers, h 8
  • Qiang Fu — 7 papers, h 15
  • Qiang Fu — 7 papers, h 5

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
20212023
most citedMaking Large Language Models Better Reasoners with Step-Aware Verifier

39 citations · 119 across the 12 of their papers we have counts for

collaborators
Showing 2023Show all

4 papers · 1 filter

cs.CL2023

How Do In-Context Examples Affect Compositional Generalization?

Shengnan An, Zeqi Lin, Qiang Fu +4

Compositional generalization--understanding unseen combinations of seen primitives--is an essential reasoning capability in human intelligence. The AI community mainly studies this…

cs.CL2023★ 2 cited

Skill-Based Few-Shot Selection for In-Context Learning

Shengnan An, Bo Zhou, Zeqi Lin +5

In-context learning is the paradigm that adapts large language models to downstream tasks by providing a few examples. Few-shot selection -- selecting appropriate examples for each…

cs.SI2023★ 36 cited

Homophily-oriented Heterogeneous Graph Rewiring

Jiayan Guo, Lun Du, Wendong Bi +6

With the rapid development of the World Wide Web (WWW), heterogeneous graphs (HG) have explosive growth. Recently, heterogeneous graph neural network (HGNN) has shown great potenti…

cs.LG2023★ 3 cited

Does Deep Learning Learn to Abstract? A Systematic Probing Framework

Shengnan An, Zeqi Lin, Bei Chen +3

Abstraction is a desirable capability for deep learning models, which means to induce abstract concepts from concrete instances and flexibly apply them beyond the learning context.…

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