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Ji Zhang — most-cited papers & profile · Large Language Models
← authors
·
overview
Ji Zhang
58
papers ·
116
citations ·
0
h-index
University of Southern Queensland · Zhejiang University of Technology
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Semantic Scholar ↗
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Most-cited papers
ModelScope-Agent: Building Your Customizable Agent System with Open-source Large Language Models
2023 · 11 citations
AdaMMS: Model Merging for Heterogeneous Multimodal Large Language Models with Unsupervised Coefficient Optimization
2025 · 1 citations
Share More, Search Less: Collaborative Parallel Thinking for Efficient Test-Time Scaling
2026
QwenLong-L1: Towards Long-Context Large Reasoning Models with Reinforcement Learning
2025
QwenLong-CPRS: Towards infty-LLMs with Dynamic Context Optimization
2025
MUSEG: Reinforcing Video Temporal Understanding via Timestamp-Aware Multi-Segment Grounding
2025
Scaling External Knowledge Input Beyond Context Windows of LLMs via Multi-Agent Collaboration
2025
Youku-mPLUG: A 10 Million Large-scale Chinese Video-Language Dataset for Pre-training and Benchmarks
2023
mPLUG-DocOwl: Modularized Multimodal Large Language Model for Document Understanding
2023
mPLUG-Owl2: Revolutionizing Multi-modal Large Language Model with Modality Collaboration
2023
mPLUG-DocOwl 1.5: Unified Structure Learning for OCR-free Document Understanding
2024
MIBench: Evaluating Multimodal Large Language Models over Multiple Images
2024
mPLUG-Owl3: Towards Long Image-Sequence Understanding in Multi-Modal Large Language Models
2024
mPLUG-DocOwl2: High-resolution Compressing for OCR-free Multi-page Document Understanding
2024
SymDPO: Boosting In-Context Learning of Large Multimodal Models with Symbol Demonstration Direct Preference Optimization
2024
Top co-authors
Fei Huang
· 14
Ming Yan
· 14
Haiyang Xu
· 9
Jiabo Ye
· 7
Jingren Zhou
· 7
Anwen Hu
· 6
Chenliang Li
· 6
Weizhou Shen
· 4
Haowei Liu
· 3
Peng Li
· 3
Qinghao Ye
· 3
Qi Qian
· 3
Topics
Training Techniques
Vision-Language
Model Architecture
Efficiency
In-Context Learning
RAG
Evaluation
Fine-Tuning
Reinforcement Learning
context