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Jonathan Frankle

Jonathan Frankle has 2 papers in our library, in 2024, most of them on Language models & pretraining; often with Christopher D. Manning, David Hall, Michihiro Yasunaga.

2 papers in our library · 2024

OpenAlex · h-index 18 · 48 works · 3,151 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 94 times in our library · ⭐ Paper Stars ranking: #6359

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

🎓Massachusetts Institute of TechnologyCambridge · United States2018–2022 · 18 papers🏢DatabricksSan Francisco2024–2025 · 4 papers🔬MIT Computer Science and Artificial Intelligence LaboratoryCambridge · United States2020–2021 · 4 papers🎓Princeton UniversityPrinceton · United States2016 · 2 papers

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.

 Fields, topics and concepts

Of its works in our library, with the number of works on each.

📚 Shelves 9The 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 workBiochemistry, Genetics and Molecular Biology · 1 workHealth Informatics · 1 workMedicine · 1 workMolecular Biology · 1 work

🧭 Topics 1Research subjects and languages the work is about.Language models & pretraining · 2 works

💡 Concepts 1Key ideas and methods explained on our concept pages.Scaling laws · 1 work

 Works by year

2020s

20242 papers

 Papers

The most cited first (OpenAlex and our library).

  1. BioMedLM: A 2.7B Parameter Language Model Trained On Biomedical Text (2024 · 79 citations)
    Frontier systems such as GPT-4 and Med-PaLM 2 perform very well on biomedical NLP. However, they are huge, expensive, reachable only through remote APIs (a problem for patient privacy) and trained on undisclosed data. The authors ask whether a much smaller, domain-focused model can be competitive. They built…
  2. Beyond Chinchilla-Optimal: Accounting for Inference in Language Model Scaling Laws (2024 · 57 citations)
    The Chinchilla scaling laws suggest how to divide a compute budget between the number of parameters and the amount of training data.

 Co-authors here

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

 Cited by

Papers of our library cite their works 94 times (of the 149,439 papers whose bibliographies we read); 23 citing papers with a page here →.

Updated

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