--- title: "William Fedus — author profile and papers" description: "William Fedus, Massachusetts Institute of Technology: 3 papers on Elamur Research (2022–2024), h-index 2 — affiliations, topics, co-authors and who cites…" canonical: "https://research.elamur.ai/free/en/authors/william-fedus/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/authors/william-fedus/) [home](https://md.research.elamur.ai/free/en/) / [authors](https://md.research.elamur.ai/free/en/authors/) / William Fedus # William Fedus William Fedus works on large language models: how they are trained, how they scale, and how their outputs can be evaluated. Their papers here ask how finetuning on instructions helps models handle tasks they were never shown, whether the inductive bias of an architecture changes its scaling behaviour, and how the factual accuracy of model answers can be measured reliably. The approach is large-scale and empirical. It involves training and finetuning models across many sizes, and building benchmarks with careful human checking. The work belongs to the area of [Language models & pretraining](https://md.research.elamur.ai/free/en/topics/language-models/). The most cited of these papers, [Scaling Instruction-Finetuned Language Models](https://md.research.elamur.ai/free/en/papers/chung-2022-scaling-instruction-finetuned-language-models/) (2022), finetunes pretrained models such as T5, PaLM and U-PaLM on a broad mixture of tasks phrased as instructions. It then examines how results shift when more tasks are added, when models grow larger, and when examples with step-by-step reasoning are mixed in. [Scaling Laws vs Model Architectures](https://md.research.elamur.ai/free/en/papers/tay-2023-scaling-laws-vs-model-architectures/) (2023) questions the habit of judging a new architecture at a single size. It tests whether gains seen at one scale hold at others, both during pretraining and once models are finetuned. The newest work turns to the reliability of what models say. [Measuring short-form factuality in large language models](https://md.research.elamur.ai/free/en/papers/wei-2024-measuring-short-form-factuality-large/) (2024), written with authors from OpenAI, presents SimpleQA. This is a benchmark of brief questions, each with one checkable answer. The questions were written adversarially against existing models and confirmed by a second trainer and by independent web sources. A prompted grader then sorts every model reply into one of three classes: right, wrong, or declined. Fedus is currently affiliated with the [Massachusetts Institute of Technology](https://md.research.elamur.ai/free/en/universities/massachusetts-institute-of-technology/). Earlier bylines list [Google](https://md.research.elamur.ai/free/en/companies/google/), the [Université de Montréal](https://md.research.elamur.ai/free/en/universities/universite-de-montreal/) and [Mila - Quebec Artificial Intelligence Institute](https://md.research.elamur.ai/free/en/labs/mila-quebec-artificial-intelligence-institute/). Frequent co-authors include [Hyung Won Chung](https://md.research.elamur.ai/free/en/authors/hyung-won-chung/), [Mostafa Dehghani](https://md.research.elamur.ai/free/en/authors/mostafa-dehghani/) and [Yi Tay](https://md.research.elamur.ai/free/en/authors/yi-tay/). William Fedus has 3 papers in our library, in 2022–2024, most of them on [Language models & pretraining](https://md.research.elamur.ai/free/en/topics/language-models/); often with [Hyung Won Chung](https://md.research.elamur.ai/free/en/authors/hyung-won-chung/), [Mostafa Dehghani](https://md.research.elamur.ai/free/en/authors/mostafa-dehghani/), [Yi Tay](https://md.research.elamur.ai/free/en/authors/yi-tay/). 3 papers in our library · 2022–2024 [OpenAlex](https://openalex.org/A5007823097) · [**h-index 2**](https://openalex.org/authors/A5007823097) · [6 works](https://openalex.org/works?filter=author.id:A5007823097) · [268 citations](https://openalex.org/works?filter=author.id:A5007823097&sort=cited_by_count:desc) OpenAlex, 2026-10-14 [📄 3 works in our library](https://md.research.elamur.ai/free/en/authors/william-fedus/#papers) · [🔗 4,883 citations in our library](https://md.research.elamur.ai/free/en/authors/william-fedus/cited-by/) · [2 more in our library without a page yet](#also) · [⭐ Paper Star rank #1022](https://md.research.elamur.ai/free/en/paper-stars/page/11/#star-1022) 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=William+Fedus) · [Semantic Scholar](https://www.semanticscholar.org/search?q=William+Fedus) · [dblp](https://dblp.org/search?q=William+Fedus) · [ACL Anthology](https://aclanthology.org/search/?q=William+Fedus) ## Affiliations [🎓**Massachusetts Institute of Technology** Cambridge · United States 2010–2012 · 4 papers*current*](https://md.research.elamur.ai/free/en/universities/massachusetts-institute-of-technology/) · [🏢**Google** Mountain View · United States 2018–2022 · 11 papers](https://md.research.elamur.ai/free/en/companies/google/) · [🎓**Université de Montréal** Montreal · Canada 2018–2021 · 5 papers](https://md.research.elamur.ai/free/en/universities/universite-de-montreal/) · [🔬**Mila - Quebec Artificial Intelligence Institute** Montreal · Canada 2018–2020 · 3 papers](https://md.research.elamur.ai/free/en/labs/mila-quebec-artificial-intelligence-institute/) 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. ## Topics [Language models & pretraining (2)](https://md.research.elamur.ai/free/en/topics/language-models/) ## Works by year ### 2020s [**2024** 1 paper](https://md.research.elamur.ai/free/en/papers/wei-2024-measuring-short-form-factuality-large/) · [**2023** 1 paper](https://md.research.elamur.ai/free/en/papers/tay-2023-scaling-laws-vs-model-architectures/) · [**2022** 1 paper](https://md.research.elamur.ai/free/en/papers/chung-2022-scaling-instruction-finetuned-language-models/) ## Papers The most cited first (OpenAlex and our library). 1. **[Scaling Instruction-Finetuned Language Models](https://md.research.elamur.ai/free/en/papers/chung-2022-scaling-instruction-finetuned-language-models/)** *(2022 · 2,176 citations)* The authors from Google seek a way to teach pretrained language models to better handle tasks they have not seen before. 2. **[Measuring short-form factuality in large language models](https://md.research.elamur.ai/free/en/papers/wei-2024-measuring-short-form-factuality-large/)** *(2024 · 147 citations)* Large language models often state false facts with confidence, and factuality is hard to measure when answers are long and full of claims. 3. **[Scaling Laws vs Model Architectures: How does Inductive Bias Influence Scaling?](https://md.research.elamur.ai/free/en/papers/tay-2023-scaling-laws-vs-model-architectures/)** *(2023 · 79 citations)* Most work on scaling laws studies only the standard Transformer. New architectures are also often judged at a single model size, such as "base", on the assumption that their gains carry over to other scales. This paper tests that assumption. The authors ask whether the inductive bias built into an architecture… ## Also in our library 2 more works of theirs are in our library without a page here yet; they will appear as we publish them. ## Co-authors here 12 of 19 co-authors with a page, the most papers together first. - [Hyung Won Chung](https://md.research.elamur.ai/free/en/authors/hyung-won-chung/) ×3 - [Mostafa Dehghani](https://md.research.elamur.ai/free/en/authors/mostafa-dehghani/) ×2 - [Yi Tay](https://md.research.elamur.ai/free/en/authors/yi-tay/) ×2 - [Albert Webson](https://md.research.elamur.ai/free/en/authors/albert-webson/) - [Amelia Glaese](https://md.research.elamur.ai/free/en/authors/amelia-glaese/) - [Barret Zoph](https://md.research.elamur.ai/free/en/authors/barret-zoph/) - [Dani Yogatama](https://md.research.elamur.ai/free/en/authors/dani-yogatama/) - [Donald Metzler](https://md.research.elamur.ai/free/en/authors/donald-metzler/) - [Jason Wei](https://md.research.elamur.ai/free/en/authors/jason-wei/) - [Jinfeng Rao](https://md.research.elamur.ai/free/en/authors/jinfeng-rao/) - [John Schulman](https://md.research.elamur.ai/free/en/authors/john-schulman/) - [Le Hou](https://md.research.elamur.ai/free/en/authors/le-hou/) ## Cited by Papers of our library cite their works 4,883 times (of the 140,884 papers whose bibliographies we read); [352 citing papers with a page here →](https://md.research.elamur.ai/free/en/authors/william-fedus/cited-by/).