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Yongkang Wu

5 papers hereh-index 5341 citations8 works total

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

author position
  • middle author5

Across the 5 of 5 papers where every author was matched, so the position is known.

fields
  • cs.CL4
  • cs.IR1
same name
  • Yongkang Wu — 5 papers, h 7
  • Yongkang Wu — 3 papers, h 2
  • Yongkang Wu — 1 paper, h 2
  • Yongkang Wu — 1 paper, h 1

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 citedRetroLLM: Empowering Large Language Models to Retrieve Fine-grained Evidence within Generation

2 citations · 5 across the 4 of their papers we have counts for

collaborators
Showing cs.CLShow all

4 papers · 1 filter

cs.CL2025

Neuro-Symbolic Query Compiler

Yuyao Zhang, Zhicheng Dou, Xiaoxi Li +5

Precise recognition of search intent in Retrieval-Augmented Generation (RAG) systems remains a challenging goal, especially under resource constraints and for complex queries with…

cs.CL2025★ 1 cited

Hierarchical Document Refinement for Long-context Retrieval-augmented Generation

Jiajie Jin, Xiaoxi Li, Guanting Dong +6

Real-world RAG applications often encounter long-context input scenarios, where redundant information and noise results in higher inference costs and reduced performance. To addres…

cs.CL2025

WebThinker: Empowering Large Reasoning Models with Deep Research Capability

Xiaoxi Li, Jiajie Jin, Guanting Dong +5

Large reasoning models (LRMs), such as OpenAI-o1 and DeepSeek-R1, demonstrate impressive long-horizon reasoning capabilities. However, their reliance on static internal knowledge l…

cs.CL2024★ 2 cited

RetroLLM: Empowering Large Language Models to Retrieve Fine-grained Evidence within Generation

Xiaoxi Li, Jiajie Jin, Yujia Zhou +4

Large language models (LLMs) exhibit remarkable generative capabilities but often suffer from hallucinations. Retrieval-augmented generation (RAG) offers an effective solution by i…

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