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researcher

Yu Huang

9 papers hereh-index 430 citations10 works total

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

author position
  • first author1
  • middle author8

Across the 9 of 9 papers where every author was matched, so the position is known.

fields
  • cs.CL4
  • cs.CV4
  • cs.AI1
same name
  • Yu Huang — 15 papers, h 4
  • Yu Huang — 13 papers, h 125
  • Yu Huang — 13 papers, h 5
  • Yu Huang — 12 papers, h 12
  • Yu Huang — 11 papers, h 15
  • Yu Huang — 9 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

collaborators
Showing cs.CLShow all

4 papers · 1 filter

cs.CL2026

Beyond the Grid: Layout-Informed Multi-Vector Retrieval with Parsed Visual Document Representations

Yibo Yan, Mingdong Ou, Yi Cao +6

Harnessing the full potential of visually-rich documents requires retrieval systems that understand not just text, but intricate layouts, a core challenge in Visual Document Retrie…

cs.CL2026

Unlocking Multimodal Document Intelligence: From Current Triumphs to Future Frontiers of Visual Document Retrieval

Yibo Yan, Jiahao Huo, Guanbo Feng +12

With the rapid proliferation of multimodal information, Visual Document Retrieval (VDR) has emerged as a critical frontier in bridging the gap between unstructured visually rich da…

cs.CL2026

CausalEmbed: Auto-Regressive Multi-Vector Generation in Latent Space for Visual Document Embedding

Jiahao Huo, Yu Huang, Yibo Yan +7

Although Multimodal Large Language Models (MLLMs) have shown remarkable potential in Visual Document Retrieval (VDR) through generating high-quality multi-vector embeddings, the su…

cs.CL2025

KnowMT-Bench: Benchmarking Knowledge-Intensive Long-Form Question Answering in Multi-Turn Dialogues

Junhao Chen, Yu Huang, Siyuan Li +7

Multi-Turn Long-Form Question Answering (MT-LFQA) is a key application paradigm of Large Language Models (LLMs) in knowledge-intensive domains. However, existing benchmarks are lim…

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