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Youssef Benchekroun

Youssef Benchekroun has 3 papers in our library, in 2023–2026, most of them on Language models & pretraining, Speech & spoken language, Evaluation & benchmarks; often with Angelo Ortiz Tandazo, Emmanuel Dupoux, Dieuwke Hupkes.

3 papers in our library · 2023–2026

OpenAlex · h-index 0 · 9 works · 0 citations OpenAlex, 2026-10-14

📄 3 works in our library · 🔗 Cited 15 times in our library · ⭐ Paper Stars ranking: #9343

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 · 5 paperscurrent🔬École Normale Supérieure - PSLParis · France2025–2026 · 3 paperscurrent🔬École des hautes études en sciences socialesParis · France2025–2026 · 3 paperscurrent🔬GIPSA-LabGrenoble · France2025–2026 · 2 paperscurrent🎓Institut polytechnique de GrenobleGrenoble · France2025–2026 · 2 paperscurrent

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine.

 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).Arts and Humanities · 3 worksComputer Science · 3 worksArtificial Intelligence · 3 worksLanguage and Linguistics · 3 worksComputer Vision and Pattern Recognition · 1 workPsychology · 1 workSignal Processing · 2 worksExperimental and Cognitive Psychology · 1 work

🧭 Topics 4Research subjects and languages the work is about.Evaluation & benchmarks · 1 workLanguage models & pretraining · 1 workReasoning · 1 workSpeech & spoken language · 1 work

 Works by year

2020s

20262 papers20231 paper

 Papers

The most cited first (OpenAlex and our library).

  1. WorldSense: A Synthetic Benchmark for Grounded Reasoning in Large Language Models (2023 · 15 citations)
    LLMs often seem to understand the physical world, but it is hard to tell whether they build coherent internal scene representations.
  2. MauBERT: Universal Phonetic Inductive Biases for Few-Shot Acoustic Units Discovery (2026 · 1 citation)
    Self-supervised speech models such as HuBERT need very large amounts of clean audio. The units they discover are also shorter and more numerous than real phonemes, and they stay sensitive to speaker and phonetic context. This limits their use for low-resource and unwritten languages.
  3. 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

10 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 15 times (of the 149,439 papers whose bibliographies we read); 5 citing papers with a page here →.

Updated

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