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Jinghui Lu

8 papers hereh-index 12412 citations15 works total

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

author position
  • first author1
  • middle author7

Across the 8 of 8 papers where every author was matched, so the position is known.

fields
  • cs.CV4
  • cs.CL3
  • cs.AI1
same name
  • Jinghui Lu — 5 papers, h 2
  • Jinghui Lu — 4 papers, h 10
  • Jinghui Lu — 3 papers, h 6
  • Jinghui Lu — 2 papers
  • Jinghui Lu — 2 papers
  • Jinghui Lu — 2 papers, h 3

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.CL2025

Benchmarking Vision-Language Models on Chinese Ancient Documents: From OCR to Knowledge Reasoning

Haiyang Yu, Yuchuan Wu, Fan Shi +18

Chinese ancient documents, invaluable carriers of millennia of Chinese history and culture, hold rich knowledge across diverse fields but face challenges in digitization and unders…

cs.CL2025

Advancing Sequential Numerical Prediction in Autoregressive Models

Xiang Fei, Jinghui Lu, Qi Sun +6

Autoregressive models have become the de facto choice for sequence generation tasks, but standard approaches treat digits as independent tokens and apply cross-entropy loss, overlo…

cs.CL2025

Prolonged Reasoning Is Not All You Need: Certainty-Based Adaptive Routing for Efficient LLM/MLLM Reasoning

Jinghui Lu, Haiyang Yu, Siliang Xu +9

Recent advancements in reasoning have significantly enhanced the capabilities of Large Language Models (LLMs) and Multimodal Large Language Models (MLLMs) across diverse tasks. How…

cs.CL2024

A Bounding Box is Worth One Token: Interleaving Layout and Text in a Large Language Model for Document Understanding

Jinghui Lu, Haiyang Yu, Yanjie Wang +9

Recently, many studies have demonstrated that exclusively incorporating OCR-derived text and spatial layouts with large language models (LLMs) can be highly effective for document…

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