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Wenqi Shao

Wenqi Shao has 2 papers in our library, in 2023–2025, most of them on Language models & pretraining, Efficient models & compression; often with Kaipeng Zhang, Mengzhao Chen, Peng Gao.

2 papers in our library · 2023–2025

OpenAlex · ORCID · h-index 17 · 102 works · 950 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 303 times in our library · 2 more in our library without a page yet · ⭐ Paper Stars ranking: #4313

The profile is the library's match of the name; two people with one name are not always told apart.

Elsewhere: Google Scholar · Semantic Scholar · dblp · ACL Anthology

 Affiliations

🔬Shanghai Artificial Intelligence LaboratoryShanghai · China2023–2026 · 44 paperscurrent🎓Shanghai Innovation InstituteShanghai · China2025–2026 · 5 paperscurrent🏢JingdongBeijing · China2026 · 2 paperscurrent🎓Chinese University of Hong KongHong Kong · China2018–2025 · 16 papers🎓Harbin Institute of TechnologyHarbin · China2023–2025 · 2 papers🎓University of Hong KongPok Fu Lam · China2023–2025 · 2 papers🎓Shanghai Open UniversityShanghai · China2024 · 2 papers🏢TencentShenzhen · China2021–2022 · 2 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 1 more institution on single bylines are not listed.

 Fields, topics and concepts

Of its works in our library, with the number of works on each.

📚 Shelves 5The field of science this work belongs to (OpenAlex fields).Arts and Humanities · 2 worksComputer Science · 2 worksArtificial Intelligence · 2 worksComputer Vision and Pattern Recognition · 2 worksLanguage and Linguistics · 2 works

🧭 Topics 2Research subjects and languages the work is about.Efficient models & compression · 2 worksLanguage models & pretraining · 2 works

 Works by year

2020s

20251 paperWenqi Shao's papers of 202520231 paperWenqi Shao's papers of 2023

 Papers

The most cited first (OpenAlex and our library).

  1. OmniQuant: Omnidirectionally Calibrated Quantization for Large Language Models (2023 · 193 citations)
    Compressing large language models to very low bit-widths usually forces a trade-off. Post-training quantization (PTQ) is cheap, but it relies on hand-tuned parameters and degrades badly at settings such as W2A16 or W4A4. Quantization-aware training (QAT) keeps accuracy but needs heavy compute and data. OmniQuant aims…
  2. EfficientQAT: Efficient Quantization-Aware Training for Large Language Models (2025 · 82 citations)
    Quantization-aware training (QAT) can compress large language models to very low bit-widths with little accuracy loss.

All works

4 works of theirs in our library, the most cited first; 2 with a page here, the others link their original until we summarise them.

  1. OmniQuant: Omnidirectionally Calibrated Quantization for Large Language ModelsarXiv:2308.13137Wenqi Shao, Mengzhao Chen, Zhaoyang Zhang, et al.2023 · 193 citations
  2. EfficientQAT: Efficient Quantization-Aware Training for Large Language ModelsAnnual Meeting of the Association for Computational Linguistics (ACL)Mengzhao Chen, Wenqi Shao, Peng Xu, et al.2025 · 82 citations
  3. HiAgent: Hierarchical Working Memory Management for Solving Long-Horizon Agent Tasks with Large Language Model ↗Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · no summary yetMengkang Hu et al.2025 · 47 citations
  4. MM-Eureka: Exploring the Frontiers of Multimodal Reasoning with Rule-based Reinforcement Learning ↗arXiv:2503.07365 · no summary yetFanqing Meng et al.2025 · 47 citations

 Co-authors here

6 co-authors with a page, the most papers together first.

Venues

Where their works here were published, the most works first, with the editions of those that have a page.

 Cited by

Papers of our library cite their works 303 times (of the 141,840 papers whose bibliographies we read); 10 citing papers with a page here →.

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