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Li Shen

22 papers hereh-index 12686 citations32 works total

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

author position
  • middle author19
  • last author2

Across the 21 of 22 papers where every author was matched, so the position is known.

fields
  • cs.LG10
  • cs.CL5
  • cs.CV3
  • cs.SE2
  • cs.AI1
  • cs.DC1
same name
  • Li Shen — 71 papers, h 29
  • Li Shen — 15 papers, h 15
  • Li Shen — 12 papers, h 8
  • Li Shen — 12 papers, h 4
  • Li Shen — 12 papers, h 6
  • Li Shen — 11 papers

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
20232025
most citedLearning from models beyond fine-tuning

68 citations · 102 across the 22 of their papers we have counts for

collaborators
Showing 2025Show all

4 papers · 1 filter

cs.SE2025

A Multi-Language Object-Oriented Programming Benchmark for Large Language Models

Shuai Wang, Liang Ding, Li Shen +4

Establishing fair and robust benchmarks is essential for evaluating intelligent code generation by large language models (LLMs). Our survey of 35 existing benchmarks uncovers three…

cs.DC2025

CoFormer: Collaborating with Heterogeneous Edge Devices for Scalable Transformer Inference

Guanyu Xu, Zhiwei Hao, Li Shen +5

The impressive performance of transformer models has sparked the deployment of intelligent applications on resource-constrained edge devices. However, ensuring high-quality service…

cs.LG2025

Refining Few-Step Text-to-Multiview Diffusion via Reinforcement Learning

Ziyi Zhang, Li Shen, Deheng Ye +5

Text-to-multiview (T2MV) diffusion models have shown great promise in generating multiple views of a scene from a single text prompt. While few-step backbones enable real-time T2MV…

cs.CV2025

Retrieval-Augmented Perception: High-Resolution Image Perception Meets Visual RAG

Wenbin Wang, Yongcheng Jing, Liang Ding +5

High-resolution (HR) image perception remains a key challenge in multimodal large language models (MLLMs). To overcome the limitations of existing methods, this paper shifts away f…

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