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Melanie Mitchell

Melanie Mitchell has 2 papers in our library, in 2021–2026, most of them on Language models & pretraining; often with Claire E. Stevenson.

2 papers in our library · 2021–2026

OpenAlex · ORCID · Wikidata · h-index 47 · 155 works · 22,112 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 56 times in our library · 2 more in our library without a page yet · ⭐ Paper Stars ranking: #6040

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

🔬Santa Fe InstituteSanta Fe · United States1993–2026 · 76 papersEmployment since 2020current🎓Portland State UniversityPortland · United States2005–2022 · 31 papersEmployment🎓University of MichiganAnn Arbor · United States1991–1994 · 6 papersEducation🔬Los Alamos National LaboratoryLos Alamos · United States1999–2000 · 4 papers🎓Brown UniversityProvidence · United StatesEducation

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 7 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 4The field of science this work belongs to (OpenAlex fields).Artificial Intelligence · 2 worksComputer Science · 2 worksArts and Humanities · 1 workLanguage and Linguistics · 1 work

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

💡 Concepts 1Key ideas and methods explained on our concept pages.Artificial intelligence · 1 work

 Works by year

2020s

20261 paper20211 paper

 Papers

The most cited first (OpenAlex and our library).

  1. Abstraction and analogy-making in artificial intelligence (2021 · 161 citations)
    Mitchell argues that today's AI, despite its advances in vision, language and robotics, still cannot form concepts, abstractions or analogies the way people do.
  2. Can Large Language Models Generalize Analogy Solving Like Children Can? (2026 · 2 citations)
    This study tests whether large language models can carry analogical reasoning into unfamiliar domains as flexibly as people, including young children.

All works

4 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. Abstraction and analogy-making in artificial intelligenceMelanie Mitchell2021 · 161 citations
  2. Probing the psychology of AI models ↗Proceedings of the National Academy of Sciences · no summary yetRichard M. Shiffrin, Melanie Mitchell2023 · 85 citations
  3. Perspectives on the State and Future of Deep Learning - 2023 ↗arXiv (Cornell University) · no summary yetMicah Goldblum et al.2023 · 5 citations
  4. Can Large Language Models Generalize Analogy Solving Like Children Can?Transactions of the Association for Computational Linguistics (TACL)Claire E. Stevenson, Alexandra Pafford, Han L. J. van der Maas, et al.2026 · 2 citations

 Co-authors here

1 co-author 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 56 times (of the 149,439 papers whose bibliographies we read); 9 citing papers with a page here →.

Updated

This page in Markdown → https://md.research.elamur.ai/free/en/authors/melanie-mitchell/