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Shuo Wang — most-cited papers & profile · Large Language Models
← authors
·
overview
Shuo Wang
111
papers ·
478
citations ·
26
h-index
Beijing Normal University · Shanghai Maritime University
Google Scholar ↗
Semantic Scholar ↗
OpenAlex ↗
Most-cited papers
INTERVENOR: Prompting The Coding Ability Of Large Language Models With The Interactive Chain Of Repair
2023 · 26 citations
Delta-come: Training-free Delta-compression With Mixed-precision For Large Language Models
2024 · 21 citations
Pragmatic Inference Chain (PIC) Improving Llms' Reasoning Of Authentic Implicit Toxic Language
2025 · 7 citations
Omgeval: An Open Multilingual Generative Evaluation Benchmark For Large Language Models
2024 · 2 citations
Process Reinforcement through Implicit Rewards
2025 · 1 citations
OneBit: Towards Extremely Low-bit Large Language Models
2024 · 1 citations
KAP: Bridging the Knowledge Selection-Runtime Consumption Gap in LLM Systems
2026
Eliminating Out-of-Domain Recommendations in LLM-based Recommender Systems: A Unified View
2025
AgentCPM-Report: Interleaving Drafting and Deepening for Open-Ended Deep Research
2026
MiniCPM-SALA: Hybridizing Sparse and Linear Attention for Efficient Long-Context Modeling
2026
Cheers: Decoupling Patch Details from Semantic Representations Enables Unified Multimodal Comprehension and Generation
2026
QiMeng-PRepair: Precise Code Repair via Edit-Aware Reward Optimization
2026
Curvzo: Adaptive Curvature-guided Sparse Zeroth-order Optimization For Efficient LLM Fine-tuning
2026
Chunks as Arms: Multi-Armed Bandit-Guided Sampling for Long-Context LLM Preference Optimization
2025
LongDPO: Unlock Better Long-form Generation Abilities for LLMs via Critique-augmented Stepwise Information
2025
Top co-authors
Zhiyuan Liu
· 8
Maosong Sun
· 7
Xu Han
· 6
Yukun Yan
· 5
Yankai Lin
· 4
Zhenghao Liu
· 4
Chaojun Xiao
· 3
Jie Zhou
· 3
Xin Cong
· 3
Yanghao Li
· 3
Yishan Li
· 3
Bingxiang He
· 2
Topics
Training Techniques
Efficiency
Model Architecture
Evaluation
Fine-Tuning
Code
Reinforcement Learning
In-Context Learning
RAG
Vision-Language