--- title: "Kaipeng Zhang — author profile and papers — Elamur Research" description: "Kaipeng Zhang, Shanghai Artificial Intelligence Laboratory: 2 papers on Elamur Research (2023–2025), h-index 18 — affiliations, topics…" canonical: "https://research.elamur.ai/free/en/authors/kaipeng-zhang/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/authors/kaipeng-zhang/) [home](https://md.research.elamur.ai/free/en/) / [authors](https://md.research.elamur.ai/free/en/authors/) / Kaipeng Zhang # Kaipeng Zhang Kaipeng Zhang has 2 papers in our library, in 2023–2025, most of them on [Language models & pretraining](https://md.research.elamur.ai/free/en/topics/language-models/), [Efficient models & compression](https://md.research.elamur.ai/free/en/topics/efficient-models/); often with [Mengzhao Chen](https://md.research.elamur.ai/free/en/authors/mengzhao-chen/), [Peng Gao](https://md.research.elamur.ai/free/en/authors/peng-gao/), [Peng Xu](https://md.research.elamur.ai/free/en/authors/peng-xu/). 2 papers in our library · 2023–2025 [OpenAlex](https://openalex.org/A5036606244) · [ORCID](https://orcid.org/0000-0001-6105-6532) · [Wikidata](https://www.wikidata.org/wiki/Q131019232) · [**h-index 18**](https://openalex.org/authors/A5036606244) · [61 works](https://openalex.org/works?filter=author.id:A5036606244) · [4,799 citations](https://openalex.org/works?filter=author.id:A5036606244&sort=cited_by_count:desc) OpenAlex, 2026-10-14 [📄 2 works in our library](https://md.research.elamur.ai/free/en/authors/kaipeng-zhang/#papers) · [🔗 Cited 272 times in our library](https://md.research.elamur.ai/free/en/authors/kaipeng-zhang/cited-by/) · [1 more in our library without a page yet](#also) · [⭐ Paper Stars ranking: #4311](https://md.research.elamur.ai/free/en/paper-stars/page/44/#star-4311) The profile is the library's match of the name; two people with one name are not always told apart. Elsewhere: [Google Scholar](https://scholar.google.com/citations?view_op=search_authors&mauthors=Kaipeng+Zhang) · [Semantic Scholar](https://www.semanticscholar.org/search?q=Kaipeng+Zhang) · [dblp](https://dblp.org/search?q=Kaipeng+Zhang) · [ACL Anthology](https://aclanthology.org/search/?q=Kaipeng+Zhang) ## Affiliations [🔬**Shanghai Artificial Intelligence Laboratory** Shanghai · China 2023–2026 · 25 papers*current*](https://md.research.elamur.ai/free/en/labs/shanghai-artificial-intelligence-laboratory/) · [🎓**The University of Tokyo** Tokyo · Japan 2019–2023 · 6 papers](https://md.research.elamur.ai/free/en/universities/the-university-of-tokyo/) · [🎓**National Taiwan University** Taipei · Taiwan 2017–2019 · 6 papers](https://md.research.elamur.ai/free/en/universities/national-taiwan-university/) · [🎓**Shanghai Innovation Institute** Shanghai · China 2025 · 4 papers](https://ror.org/01fatbw66) · [🏛️**Chinese Academy of Sciences** Beijing · China 2016–2019 · 3 papers](https://md.research.elamur.ai/free/en/government/chinese-academy-of-sciences/) · [🔬**Shenzhen Institutes of Advanced Technology** Shenzhen · China 2016–2019 · 3 papers](https://md.research.elamur.ai/free/en/labs/shenzhen-institutes-of-advanced-technology/) From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 3 more institutions on single bylines are not listed. ## Fields, topics and concepts Of its works in our library, with the number of works on each. 📚 Shelves 5 The field of science this work belongs to (OpenAlex fields).[Arts and Humanities · 2 works](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/) · [Computer Science · 2 works](https://md.research.elamur.ai/free/en/shelves/computer-science/) · [Artificial Intelligence · 2 works](https://md.research.elamur.ai/free/en/shelves/computer-science/artificial-intelligence/) · [Computer Vision and Pattern Recognition · 2 works](https://md.research.elamur.ai/free/en/shelves/computer-science/computer-vision-and-pattern-recognition/) · [Language and Linguistics · 2 works](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/language-and-linguistics/) 🧭 Topics 2 Research subjects and languages the work is about.[Efficient models & compression · 2 works](https://md.research.elamur.ai/free/en/topics/efficient-models/) · [Language models & pretraining · 2 works](https://md.research.elamur.ai/free/en/topics/language-models/) ## Works by year ### 2020s [**2025** 1 paper Kaipeng Zhang's papers of 2025](https://md.research.elamur.ai/free/en/papers/chen-2025-efficientqat-efficient-quantization-aware-training/) · [**2023** 1 paper Kaipeng Zhang's papers of 2023](https://md.research.elamur.ai/free/en/papers/shao-2023-omniquant-omnidirectionally-calibrated-quantization-large/) ## Papers The most cited first (OpenAlex and our library). 1. **[OmniQuant: Omnidirectionally Calibrated Quantization for Large Language Models](https://md.research.elamur.ai/free/en/papers/shao-2023-omniquant-omnidirectionally-calibrated-quantization-large/)** *(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](https://md.research.elamur.ai/free/en/papers/chen-2025-efficientqat-efficient-quantization-aware-training/)** *(2025 · 82 citations)* Quantization-aware training (QAT) can compress large language models to very low bit-widths with little accuracy loss. ## All works 3 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 Models arXiv:2308.13137**Wenqi Shao, Mengzhao Chen, Zhaoyang Zhang, et al.*2023 · 193 citations*](https://md.research.elamur.ai/free/en/papers/shao-2023-omniquant-omnidirectionally-calibrated-quantization-large/) 2. **[EfficientQAT: Efficient Quantization-Aware Training for Large Language Models](https://md.research.elamur.ai/free/en/papers/chen-2025-efficientqat-efficient-quantization-aware-training/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Mengzhao Chen, Wenqi Shao, Peng Xu, et al.*2025 · 82 citations* 3. **[MM-Eureka: Exploring the Frontiers of Multimodal Reasoning with Rule-based Reinforcement Learning ↗](https://arxiv.org/abs/2503.07365) arXiv:2503.07365 · no summary yet**Fanqing Meng et al.*2025 · 47 citations* ## Co-authors here 6 co-authors with a page, the most papers together first. - [Mengzhao Chen](https://md.research.elamur.ai/free/en/authors/mengzhao-chen/) ×2 - [Peng Gao](https://md.research.elamur.ai/free/en/authors/peng-gao/) ×2 - [Peng Xu](https://md.research.elamur.ai/free/en/authors/peng-xu/) ×2 - [Ping Luo](https://md.research.elamur.ai/free/en/authors/ping-luo/) ×2 - [Wenqi Shao](https://md.research.elamur.ai/free/en/authors/wenqi-shao/) ×2 - [Yu Qiao](https://md.research.elamur.ai/free/en/authors/yu-qiao/) ## Venues Where their works here were published, the most works first, with the editions of those that have a page. - [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/): [ACL 2025](https://md.research.elamur.ai/free/en/venues/acl/2025/) ## Cited by Papers of our library cite their works 272 times (of the 141,840 papers whose bibliographies we read); [9 citing papers with a page here →](https://md.research.elamur.ai/free/en/authors/kaipeng-zhang/cited-by/). Updated 24 October 2026