--- title: "Yanqi Zhou — author profile and papers" description: "Yanqi Zhou, Shanghai University: 3 papers on Elamur Research (2017–2021), h-index 23 — affiliations, topics, co-authors and who cites them in our library." canonical: "https://research.elamur.ai/free/en/authors/yanqi-zhou/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/authors/yanqi-zhou/) [home](https://md.research.elamur.ai/free/en/) / [authors](https://md.research.elamur.ai/free/en/authors/) / Yanqi Zhou # Yanqi Zhou Yanqi Zhou works on large neural networks for language and on how such systems behave as they grow. Their papers here return to the same practical questions: how far performance improves with more data and larger models, how to compare training choices fairly, and how to add model capacity without a matching rise in compute. The methods are mostly large empirical studies and new model designs, centred on [Language models & pretraining](https://md.research.elamur.ai/free/en/topics/language-models/). The most cited of these papers, [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://md.research.elamur.ai/free/en/papers/raffel-2019-exploring-limits-transfer-learning-unified/) (2019), treats every language task as turning one text into another. That framing lets a single model and training setup serve tasks from translation to summarization. On this basis the paper systematically compares pre-training objectives, architectures, corpora and fine-tuning strategies, and it introduces the C4 web corpus. An earlier study, [Deep Learning Scaling is Predictable, Empirically](https://md.research.elamur.ai/free/en/papers/hestness-2017-deep-learning-scaling-predictable-empirically/) (2017), trained models for speech, vision, language modeling and translation on growing slices of data. It found that accuracy gains follow power-law curves that can be measured and forecast, and that these curves do not match the exponents classical theory predicts. The newest work here, [GLaM: Efficient Scaling of Language Models with Mixture-of-Experts](https://md.research.elamur.ai/free/en/papers/du-2021-glam-efficient-scaling-language-models/) (2021), turns to the cost of very large decoder-only models. It replaces some feed-forward layers with sparsely gated expert layers, so that each token activates only a small part of the network. The models are trained on a large, quality-filtered text collection. Zhou is currently at [Google](https://md.research.elamur.ai/free/en/companies/google/). Earlier bylines list [Baidu](https://md.research.elamur.ai/free/en/companies/baidu/), where the scaling study was done, and [Princeton University](https://md.research.elamur.ai/free/en/universities/princeton-university/). Co-authors on these papers include [Colin Raffel](https://md.research.elamur.ai/free/en/authors/colin-raffel/) and [Sharan Narang](https://md.research.elamur.ai/free/en/authors/sharan-narang/), as well as Joel Hestness and Dmitry Lepikhin. Yanqi Zhou has 3 papers in our library, in 2017–2021, most of them on [Language models & pretraining](https://md.research.elamur.ai/free/en/topics/language-models/); often with [Sharan Narang](https://md.research.elamur.ai/free/en/authors/sharan-narang/), [Adam Roberts](https://md.research.elamur.ai/free/en/authors/adam-roberts/), [Adams Wei Yu](https://md.research.elamur.ai/free/en/authors/adams-wei-yu/). 3 papers in our library · 2017–2021 [OpenAlex](https://openalex.org/A5081567725) · [ORCID](https://orcid.org/0000-0003-2051-7616) · [**h-index 23**](https://openalex.org/authors/A5081567725) · [76 works](https://openalex.org/works?filter=author.id:A5081567725) · [6,462 citations](https://openalex.org/works?filter=author.id:A5081567725&sort=cited_by_count:desc) OpenAlex, 2026-10-14 [📄 3 works in our library](https://md.research.elamur.ai/free/en/authors/yanqi-zhou/#papers) · [🔗 8,538 citations in our library](https://md.research.elamur.ai/free/en/authors/yanqi-zhou/cited-by/) · [1 more in our library without a page yet](#also) · [⭐ Paper Star rank #264](https://md.research.elamur.ai/free/en/paper-stars/page/3/#star-264) 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=Yanqi+Zhou) · [Semantic Scholar](https://www.semanticscholar.org/search?q=Yanqi+Zhou) · [dblp](https://dblp.org/search?q=Yanqi+Zhou) · [ACL Anthology](https://aclanthology.org/search/?q=Yanqi+Zhou) ## Affiliations [🏢**Google** Mountain View · United States 2019–2026 · 18 papers*current*](https://md.research.elamur.ai/free/en/companies/google/) · [🎓**Shanghai University** Shanghai · China 2021–2026 · 3 papers*current*](https://md.research.elamur.ai/free/en/universities/shanghai-university/) · [🔬**State Key Laboratory Fluid Power and Mechatronic Systems** Hangzhou · China 2026 · 1 paper*current*](https://ror.org/01h0g5x34) · [🎓**Zhejiang University** Hangzhou · China 2026 · 1 paper*current*](https://md.research.elamur.ai/free/en/universities/zhejiang-university/) · [🎓**Princeton University** Princeton · United States 2014–2017 · 15 papers](https://md.research.elamur.ai/free/en/universities/princeton-university/) · [🎓**Heilongjiang University** Harbin · China 2020–2022 · 9 papers](https://ror.org/04zyhq975) · [🏢**Baidu** Beijing · China 2018–2021 · 7 papers](https://md.research.elamur.ai/free/en/companies/baidu/) · [🎓**Ningbo City College of Vocational Technology** Ningbo · China 2025 · 3 papers](https://ror.org/00vkb8q56) From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 5 more institutions on single bylines are not listed. ## Topics [Language models & pretraining (1)](https://md.research.elamur.ai/free/en/topics/language-models/) ## Works by year ### 2020s [**2021** 1 paper](https://md.research.elamur.ai/free/en/papers/du-2021-glam-efficient-scaling-language-models/) ### 2010s [**2019** 1 paper](https://md.research.elamur.ai/free/en/papers/raffel-2019-exploring-limits-transfer-learning-unified/) · [**2017** 1 paper](https://md.research.elamur.ai/free/en/papers/hestness-2017-deep-learning-scaling-predictable-empirically/) ## Papers The most cited first (OpenAlex and our library). 1. **[Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://md.research.elamur.ai/free/en/papers/raffel-2019-exploring-limits-transfer-learning-unified/)** *(2019 · 7,433 citations)* The authors from Google propose a unified view of transfer learning in NLP. They cast every language task as the conversion of input text into output text: translation, classification, question answering, summarization. This makes it possible to use a single model, a single loss function and the same hyperparameters… 2. **[Deep Learning Scaling is Predictable, Empirically](https://md.research.elamur.ai/free/en/papers/hestness-2017-deep-learning-scaling-predictable-empirically/)** *(2017 · 497 citations)* Practitioners widely assume that more data and bigger models improve accuracy, but there was no reliable way to forecast how much gain a given increase in training data would bring. 3. **[GLaM: Efficient Scaling of Language Models with Mixture-of-Experts](https://md.research.elamur.ai/free/en/papers/du-2021-glam-efficient-scaling-language-models/)** *(2021 · 474 citations)* Dense language models such as GPT-3 showed that scale enables strong in-context learning, but training and serving them costs a great deal of compute and energy. ## Also in our library 1 more work of theirs is in our library without a page here yet; it will appear as we publish them. ## Co-authors here 12 of 19 co-authors with a page, the most papers together first. - [Sharan Narang](https://md.research.elamur.ai/free/en/authors/sharan-narang/) ×2 - [Adam Roberts](https://md.research.elamur.ai/free/en/authors/adam-roberts/) - [Adams Wei Yu](https://md.research.elamur.ai/free/en/authors/adams-wei-yu/) - [Andrew M. Dai](https://md.research.elamur.ai/free/en/authors/andrew-m-dai/) - [Barret Zoph](https://md.research.elamur.ai/free/en/authors/barret-zoph/) - [Colin Raffel](https://md.research.elamur.ai/free/en/authors/colin-raffel/) - [Dmitry Lepikhin](https://md.research.elamur.ai/free/en/authors/dmitry-lepikhin/) - [Greg Diamos](https://md.research.elamur.ai/free/en/authors/greg-diamos/) - [Heewoo Jun](https://md.research.elamur.ai/free/en/authors/heewoo-jun/) - [Joel Hestness](https://md.research.elamur.ai/free/en/authors/joel-hestness/) - [Katherine Lee](https://md.research.elamur.ai/free/en/authors/katherine-lee/) - [Maxim Krikun](https://md.research.elamur.ai/free/en/authors/maxim-krikun/) ## Cited by Papers of our library cite their works 8,538 times (of the 140,884 papers whose bibliographies we read); [669 citing papers with a page here →](https://md.research.elamur.ai/free/en/authors/yanqi-zhou/cited-by/).