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Melanie Kambadur

Melanie Kambadur has 2 papers in our library, in 2022–2026, most of them on Language models & pretraining, Multilingual & cross-lingual NLP, Code & programming languages; often with Adina Williams, Eric Michael Smith, Sebastian Ruder.

2 papers in our library · 2022–2026

OpenAlex · ORCID · h-index 10 · 33 works · 3,062 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 7,327 times in our library · 1 more in our library without a page yet · ⭐ Paper Stars ranking: #6857

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 States2026 · 2 paperscurrent🎓Columbia UniversityNew York · United States2012–2016 · 14 papers🏢MetaMenlo Park · United States2022–2025 · 4 papersEmployment

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.

 Fields, topics and concepts

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

📚 Shelves 8The field of science this work belongs to (OpenAlex fields).Artificial Intelligence · 2 worksArts and Humanities · 2 worksComputer Science · 2 worksLanguage and Linguistics · 2 worksSafety Research · 1 workSocial Sciences · 1 workComputer Science Applications · 1 workInformation Systems · 1 work

🧭 Topics 3Research subjects and languages the work is about.Language models & pretraining · 1 workCode & programming languages · 1 workMultilingual & cross-lingual NLP · 1 work

 Works by year

2020s

20261 paper20221 paper

 Papers

The most cited first (OpenAlex and our library).

  1. “I'm sorry to hear that”: Finding New Biases in Language Models with a Holistic Descriptor Dataset (2022 · 167 citations)
    Existing bias benchmarks for language models cover only a few demographic groups and usually come with fixed tests that assume in advance which biases a model might have.
  2. Parallel-SFT: Improving Zero-Shot Cross-Programming-Language Transfer for Code RL (2026)
    LLMs write code well in popular programming languages, but weaker data coverage hurts them in less common ones.

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. “I'm sorry to hear that”: Finding New Biases in Language Models with a Holistic Descriptor DatasetConference on Empirical Methods in Natural Language Processing (EMNLP)Eric Michael Smith, Melissa Hall, Melanie Kambadur, et al.2022 · 167 citations
  2. A Theory on Adam Instability in Large-Scale Machine Learning ↗arXiv:2304.09871 · no summary yetIgor Molybog et al.2023 · 15 citations
  3. Parallel-SFT: Improving Zero-Shot Cross-Programming-Language Transfer for Code RLFindings of the Association for Computational LinguisticsZhaofeng Wu, Shiqi Wang, Boya Peng, et al.2026

 Co-authors here

6 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 7,327 times (of the 149,439 papers whose bibliographies we read); 1,440 citing papers with a page here →.

Updated

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