🧑‍🔬 Scott Gray

Affiliations

🏢OpenAISan Francisco · United States2020–2021 · 3 papers

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.

Topics

Language models & pretraining1Vision & language1

Works by year

2020s

Papers

The most cited first (OpenAlex and our library).

1. Scaling Laws for Neural Language Models (2020 · 1 814 citations)
   The authors from OpenAI study how the quality of a language model depends on its scale. They measure quality by the cross-entropy loss on test data. To this end, they trained a large series of Transformer models on the WebText2 corpus, ranging from thousands to roughly a billion parameters, excluding embeddings. In…
2. Zero-Shot Text-to-Image Generation (2021 · 1 423 citations)
   The paper asks whether text-to-image synthesis can advance through scale rather than through specialized architectures, auxiliary losses or extra annotations such as segmentation masks.
3. Scaling Laws for Autoregressive Generative Modeling (2020 · 115 citations)
   The authors from OpenAI examine whether the scaling laws previously found for language models also hold for other data types.

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

• Alec Radford ×2
• Jared Kaplan ×2
• Mark Chen ×2
• Tom B. Brown ×2
• Tom Henighan ×2
• Aditya Ramesh
• Benjamin Mann
• Chris Hallacy
• Christopher Hesse
• Dario Amodei
• Gabriel Goh
• Heewoo Jun

Cited by

Papers of our library cite their works 4 412 times (of the 140 884 papers whose bibliographies we read); 273 citing papers with a page here →.

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