--- title: "Chaofan Tao · Huawei Technologies — author profile and papers" description: "Chaofan Tao, Huawei Technologies: 3 papers on Elamur Research (2024–2026), h-index 11 — affiliations, topics, co-authors and who cites…" canonical: "https://research.elamur.ai/free/en/authors/chaofan-tao/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/authors/chaofan-tao/) [home](https://md.research.elamur.ai/free/en/) / [authors](https://md.research.elamur.ai/free/en/authors/) / Chaofan Tao # Chaofan Tao Chaofan Tao has 3 papers in our library, in 2024–2026, 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 [Ngai Wong](https://md.research.elamur.ai/free/en/authors/ngai-wong/), [Angel X. Chang](https://md.research.elamur.ai/free/en/authors/angel-x-chang/), [Chenming Shang](https://md.research.elamur.ai/free/en/authors/chenming-shang/). 3 papers in our library · 2024–2026 [OpenAlex](https://openalex.org/A5006519974) · [ORCID](https://orcid.org/0000-0002-6093-0854) · [**h-index 11**](https://openalex.org/authors/A5006519974) · [64 works](https://openalex.org/works?filter=author.id:A5006519974) · [963 citations](https://openalex.org/works?filter=author.id:A5006519974&sort=cited_by_count:desc) OpenAlex, 2026-10-14 [📄 3 works in our library](https://md.research.elamur.ai/free/en/authors/chaofan-tao/#papers) · [🔗 Cited 160 times in our library](https://md.research.elamur.ai/free/en/authors/chaofan-tao/cited-by/) · [4 more in our library without a page yet](#also) · [⭐ Paper Stars ranking: #7804](https://md.research.elamur.ai/free/en/paper-stars/page/79/#star-7804) 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=Chaofan+Tao) · [Semantic Scholar](https://www.semanticscholar.org/search?q=Chaofan+Tao) · [dblp](https://dblp.org/search?q=Chaofan+Tao) · [ACL Anthology](https://aclanthology.org/search/?q=Chaofan+Tao) ## Affiliations [🎓**University of Hong Kong** Pok Fu Lam · China 2020–2026 · 34 papers*current*](https://md.research.elamur.ai/free/en/universities/university-of-hong-kong/) · [🏢**Huawei Technologies** Shenzhen · China 2026 · 4 papers*current*](https://md.research.elamur.ai/free/en/companies/huawei-technologies/) · [🏢**Huawei Technologies** Basingstoke · United Kingdom 2026 · 2 papers*current*](https://ror.org/056gzgs71) · [🎓**University of Electronic Science and Technology of China** Chengdu · China 2019–2020 · 3 papers](https://md.research.elamur.ai/free/en/universities/university-of-electronic-science-and-technology-of-china/) · [🎓**Duke University** Durham · United States 2018–2021 · 2 papers](https://md.research.elamur.ai/free/en/universities/duke-university/) From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. ## 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 · 3 works](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/) · [Computer Science · 3 works](https://md.research.elamur.ai/free/en/shelves/computer-science/) · [Artificial Intelligence · 3 works](https://md.research.elamur.ai/free/en/shelves/computer-science/artificial-intelligence/) · [Language and Linguistics · 3 works](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/language-and-linguistics/) · [Computer Vision and Pattern Recognition · 2 works](https://md.research.elamur.ai/free/en/shelves/computer-science/computer-vision-and-pattern-recognition/) 🧭 Topics 2 Research subjects and languages the work is about.[Efficient models & compression · 1 work](https://md.research.elamur.ai/free/en/topics/efficient-models/) · [Language models & pretraining · 1 work](https://md.research.elamur.ai/free/en/topics/language-models/) 💡 Concepts 1 Key ideas and methods explained on our concept pages.[Scaling laws · 1 work](https://md.research.elamur.ai/free/en/concepts/scaling-laws/) ## Works by year ### 2020s [**2026** 1 paper](https://md.research.elamur.ai/free/en/authors/chaofan-tao/year/2026/) · [**2025** 1 paper](https://md.research.elamur.ai/free/en/authors/chaofan-tao/year/2025/) · [**2024** 1 paper](https://md.research.elamur.ai/free/en/authors/chaofan-tao/year/2024/) ## Papers The most cited first (OpenAlex and our library). 1. **[Rethinking Kullback-Leibler Divergence in Knowledge Distillation for Large Language Models](https://md.research.elamur.ai/free/en/papers/wu-2025-rethinking-kullback-leibler-divergence-knowledge/)** *(2025 · 47 citations)* Distilling large language models usually means training a student to match the teacher's next-token distribution with a Kullback-Leibler loss. 2. **[Scaling Laws with Vocabulary: Larger Models Deserve Larger Vocabularies](https://md.research.elamur.ai/free/en/papers/tao-2024-scaling-laws-vocabulary-larger-models/)** *(2024 · 46 citations)* Standard scaling laws for LLMs predict loss from parameter count and training data but treat vocabulary size as fixed. 3. **[Find Your Optimal Teacher: Personalized Data Synthesis via Router-Guided Multi-Teacher Distillation](https://md.research.elamur.ai/free/en/papers/zhang-2026-find-your-optimal-teacher-personalized/)** *(2026 · 13 citations)* Small LLMs are usually distilled by fine-tuning them on outputs from the most powerful available teacher. Prior work shows this can backfire: very strong teachers may produce answers that are too complex or too far from what the student can absorb. Earlier remedies (Mix, CAR) still make every candidate teacher answer… ## All works 7 works of theirs in our library, the most cited first; 3 with a page here, the others link their original until we summarise them. 1. **[This Looks Like That: Deep Learning for Interpretable Image Recognition ↗](https://arxiv.org/abs/1806.10574) arXiv (Cornell University) · no summary yet**Chaofan Chen et al.*2018 · 590 citations* 2. **[A case-based interpretable deep learning model for classification of mass lesions in digital mammography ↗](https://doi.org/10.1038/s42256-021-00423-x) Nature Machine Intelligence · no summary yet**Alina Jade Barnett et al.*2021 · 164 citations* 3. **[Rethinking Kullback-Leibler Divergence in Knowledge Distillation for Large Language Models](https://md.research.elamur.ai/free/en/papers/wu-2025-rethinking-kullback-leibler-divergence-knowledge/) [International Conference on Computational Linguistics (COLING)](https://md.research.elamur.ai/free/en/venues/coling/)**Taiqiang Wu, Chaofan Tao, Jiahao Wang, et al.*2025 · 47 citations* 4. [**Scaling Laws with Vocabulary: Larger Models Deserve Larger Vocabularies arXiv:2407.13623**Chaofan Tao, Qian Liu, Longxu Dou, et al.*2024 · 46 citations*](https://md.research.elamur.ai/free/en/papers/tao-2024-scaling-laws-vocabulary-larger-models/) 5. **[IAIA-BL: A Case-based Interpretable Deep Learning Model for Classification of Mass Lesions in Digital Mammography ↗](https://arxiv.org/abs/2103.12308) arXiv (Cornell University) · no summary yet**Alina Jade Barnett et al.*2021 · 15 citations* 6. **[Find Your Optimal Teacher: Personalized Data Synthesis via Router-Guided Multi-Teacher Distillation](https://md.research.elamur.ai/free/en/papers/zhang-2026-find-your-optimal-teacher-personalized/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Hengyuan Zhang, Shiping Yang, Xiao Liang, et al.*2026 · 13 citations* 7. **[Interpretable Mammographic Image Classification using Case-Based Reasoning and Deep Learning ↗](https://arxiv.org/abs/2107.05605) arXiv (Cornell University) · no summary yet**Alina Jade Barnett et al.*2021 · 7 citations* ## Co-authors here 12 of 20 co-authors with a page, the most papers together first. - [Ngai Wong](https://md.research.elamur.ai/free/en/authors/ngai-wong/) ×3 - [Angel X. Chang](https://md.research.elamur.ai/free/en/authors/angel-x-chang/) - [Chenming Shang](https://md.research.elamur.ai/free/en/authors/chenming-shang/) - [Hayden Kwok-Hay So](https://md.research.elamur.ai/free/en/authors/hayden-kwok-hay-so/) - [Hengyuan Zhang](https://md.research.elamur.ai/free/en/authors/hengyuan-zhang/) - [Jiahao Wang](https://md.research.elamur.ai/free/en/authors/jiahao-wang/) - [Jing Xiong](https://md.research.elamur.ai/free/en/authors/jing-xiong/) - [Longxu Dou](https://md.research.elamur.ai/free/en/authors/longxu-dou/) - [Min Lin](https://md.research.elamur.ai/free/en/authors/min-lin/) - [Niklas Muennighoff](https://md.research.elamur.ai/free/en/authors/niklas-muennighoff/) - [Ping Luo](https://md.research.elamur.ai/free/en/authors/ping-luo/) - [Qian Liu](https://md.research.elamur.ai/free/en/authors/qian-liu/) ## 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 2026](https://md.research.elamur.ai/free/en/venues/acl/2026/) - [International Conference on Computational Linguistics (COLING)](https://md.research.elamur.ai/free/en/venues/coling/): [COLING 2025](https://md.research.elamur.ai/free/en/venues/coling/2025/) - Nature Machine Intelligence ## Cited by Papers of our library cite their works 160 times (of the 149,439 papers whose bibliographies we read); [33 citing papers with a page here →](https://md.research.elamur.ai/free/en/authors/chaofan-tao/cited-by/). Updated 2 November 2026