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Cyprien de Masson d'Autume

Cyprien de Masson d'Autume has 2 papers in our library, in 2019–2024, most of them on Language models & pretraining, Evaluation & benchmarks, Vision & language; often with Dani Yogatama, Angeliki Lazaridou, Chris Dyer.

2 papers in our library · 2019–2024

OpenAlex · ORCID · h-index 14 · 19 works · 1,930 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 1,404 times in our library · 1 more in our library without a page yet · ⭐ Paper Stars ranking: #5631

The profile is the library's match of the name; two people with one name are not always told apart.

Elsewhere: Google Scholar · Semantic Scholar · dblp · ACL Anthology

 Affiliations

🏢GoogleMountain View · United States2019–2021 · 6 papers🏢Google DeepMindLondon · United Kingdom2016–2022 · 2 papersEmployment 2016–2022 · Staff Research Engineer, Research Engineering

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 2 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 5The field of science this work belongs to (OpenAlex fields).Arts and Humanities · 2 worksComputer Science · 2 worksArtificial Intelligence · 2 worksLanguage and Linguistics · 2 worksComputer Vision and Pattern Recognition · 1 work

🧭 Topics 3Research subjects and languages the work is about.Evaluation & benchmarks · 1 workLanguage models & pretraining · 1 workVision & language · 1 work

 Works by year

2020s

20241 paper

2010s

20191 paper

 Papers

The most cited first (OpenAlex and our library).

  1. Learning and Evaluating General Linguistic Intelligence (2019 · 248 citations)
    The authors argue that standard leaderboard scores overstate progress in NLP. They propose a notion of "general linguistic intelligence": a system's capacity to reuse what it already knows about a language's words, grammar, meaning and usage so it can master unfamiliar tasks from few examples. To measure this, they…
  2. Reka Core, Flash, and Edge: A Series of Powerful Multimodal Language Models (2024 · 42 citations)
    The Reka Team presents three multimodal language models trained from scratch: Edge (7B, dense), Flash (21B, dense) and Core, the largest, whose training was still ongoing when the report was written.

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. Scaling Language Models: Methods, Analysis & Insights from Training Gopher ↗arXiv:2112.11446 · no summary yetJack W. Rae et al.2021 · 928 citations
  2. Learning and Evaluating General Linguistic IntelligencearXiv:1901.11373Dani Yogatama, Cyprien de Masson d'Autume, Jerome Connor, et al.2019 · 248 citations
  3. Reka Core, Flash, and Edge: A Series of Powerful Multimodal Language ModelsarXiv:2404.12387Reka Team: Aitor Ormazabal, Che Zheng, Cyprien de Masson d'Autume, et al.2024 · 42 citations

 Co-authors here

12 of 15 co-authors with a page, the most papers together first.

Venues

Where their works here were published, the most works first, with the editions of those that have a page.

 Cited by

Papers of our library cite their works 1,404 times (of the 149,439 papers whose bibliographies we read); 438 citing papers with a page here →.

Updated

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