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Deqing Yang

18 papers hereh-index 7129 citations22 works total

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

author position
  • middle author5
  • last author11

Across the 16 of 18 papers where every author was matched, so the position is known.

fields
  • cs.CL12
  • cs.IR4
  • cs.AI1
  • cs.LG1
same name
  • Deqing Yang — 15 papers, h 10
  • Deqing Yang — 6 papers, h 3
  • Deqing Yang — 6 papers, h 13
  • Deqing Yang — 4 papers, h 2
  • Deqing Yang — 1 paper, h 2
  • Deqing Yang — 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
20242026
most citedSkeletons Matter: Dynamic Data Augmentation for Text-to-Query

1 citations · 1 across the 6 of their papers we have counts for

collaborators
Showing cs.IRShow all

4 papers · 1 filter

cs.IR2026

When RAG Meets Query Planning: Logical Query Trees for Resolving Exploratory Reasoning Problems

Ganlin Xu, Linghao Zhang, Zhitao Yin +7

Retrieval-Augmented Generation (RAG) effectively grounds large language models (LLMs) in external knowledge but struggles with \textbf{exploratory reasoning problems (ERPs)} that a…

cs.IR2026

What Makes an Ideal Quote? Recommending "Unexpected yet Rational" Quotations via Novelty

Bowei Zhang, Jin Xiao, Guanglei Yue +4

Quotation recommendation aims to enrich writing by suggesting quotes that complement a given context, yet existing systems mostly optimize surface-level topical relevance and ignor…

cs.IR2025

ComLQ: Benchmarking Complex Logical Queries in Information Retrieval

Ganlin Xu, Zhitao Yin, Linghao Zhang +6

Information retrieval (IR) systems play a critical role in navigating information overload across various applications. Existing IR benchmarks primarily focus on simple queries tha…

cs.IR2025

GORACS: Group-level Optimal Transport-guided Coreset Selection for LLM-based Recommender Systems

Tiehua Mei, Hengrui Chen, Peng Yu +2

Although large language models (LLMs) have shown great potential in recommender systems, the prohibitive computational costs for fine-tuning LLMs on entire datasets hinder their su…

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