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Phillip Rust

Phillip Rust has 2 papers in our library, in 2021–2026, most of them on Language models & pretraining, Multilingual & cross-lingual NLP, Tokenization & segmentation; often with Angelo Ortiz Tandazo, Iryna Gurevych, Ivan Vulić.

2 papers in our library · 2021–2026

OpenAlex · ORCID · h-index 7 · 22 works · 365 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 458 times in our library · ⭐ Paper Stars ranking: #4066

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

🔬Centre National de la Recherche ScientifiqueParis · France2025–2026 · 3 paperscurrent🔬École Normale Supérieure - PSLParis · France2025–2026 · 3 paperscurrent🔬École des hautes études en sciences socialesParis · France2025–2026 · 3 paperscurrent🎓University of CopenhagenCopenhagen · Denmark2021–2025 · 15 papersEmployment until 2025🎓Technische Universität DarmstadtDarmstadt · Germany2020–2021 · 2 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 1 more institution 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).Artificial Intelligence · 2 worksArts and Humanities · 2 worksComputer Science · 2 worksLanguage and Linguistics · 2 worksSignal Processing · 1 work

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

💡 Concepts 2Key ideas and methods explained on our concept pages.Subword tokenization · 1 workTokenization · 1 work

 Works by year

2020s

20261 paper20211 paper

 Papers

The most cited first (OpenAlex and our library).

  1. How Good is Your Tokenizer? On the Monolingual Performance of Multilingual Language Models (2021 · 449 citations)
    New monolingual BERT models keep being released for languages that multilingual models already cover. The usual reason given is that multilingual models underperform on single-language tasks, but this claim has rarely been tested rigorously. Rust et al. test it with a controlled comparison of mBERT and existing…
  2. SpidR-Adapt: A Universal Speech Representation Model for Few-Shot Adaptation (2026)
    Self-supervised speech models such as HuBERT and WavLM need thousands of hours of audio to learn useful linguistic units. Infants manage this with a few hundred hours. SpidR-Adapt aims to narrow that gap, so that speech units can be learned for a new language from very little unlabeled audio.

 Co-authors here

7 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 458 times (of the 149,439 papers whose bibliographies we read); 138 citing papers with a page here →.

Updated

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