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Bettina Messmer

Bettina Messmer has 2 papers in our library, in 2025–2026, most of them on Language models & pretraining, Multilingual & cross-lingual NLP; often with Alejandro Hernández-Cano, Alexander Hägele, Amir Hossein Kargaran.

2 papers in our library · 2025–2026

OpenAlex · h-index 11 · 21 works · 1,051 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 148 times in our library · 1 more in our library without a page yet · ⭐ Paper Stars ranking: #4813

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

🎓École Polytechnique Fédérale de LausanneLausanne · Switzerland2023–2026 · 6 paperscurrent🎓University of BernBern · Switzerland1994–2000 · 11 papers🏢SwisscomBern · Switzerland1998–2001 · 2 papers

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

 Concepts, shelves and topics

Of its works in our library: the number of works on each and its rating among them.

📚 Shelves 5The field of science this work belongs to (OpenAlex fields).Arts and Humanities · 2 worksComputer Science · 2 worksArtificial Intelligence · 2 worksLanguage and Linguistics · 2 worksInformation Systems · 1 work

🧭 Topics 2Research subjects and languages the work is about.Language models & pretraining · 2 worksMultilingual & cross-lingual NLP · 1 work

 Works by year

2020s

20261 paperApertus: Democratizing Open and Compliant LLMs for Global Language Environments20251 paperFineWeb2: One Pipeline to Scale Them All — Adapting Pre-Training Data Processing to Every Language

 Papers

The most cited first (OpenAlex and our library).

  1. FineWeb2: One Pipeline to Scale Them All — Adapting Pre-Training Data Processing to Every Language (2025 · 97 citations)
    Building strong multilingual LLMs is hard mainly because data-cleaning recipes tuned for English do not transfer well.
  2. Apertus: Democratizing Open and Compliant LLMs for Global Language Environments (2026 · 67 citations)
    Most "open" LLMs release only their weights. Their training data stays undisclosed, may ignore content owners' rights, and is heavily skewed toward English and a few high-resource languages. Apertus responds with a fully open suite of 8B and 70B models, both base and Instruct. Pretraining used 15T tokens drawn only…

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. FineWeb2: One Pipeline to Scale Them All — Adapting Pre-Training Data Processing to Every LanguagearXiv:2506.20920Guilherme Penedo, Hynek Kydliček, Vinko Sabolčec, et al.2025 · 97 citations
  2. Apertus: Democratizing Open and Compliant LLMs for Global Language EnvironmentsAnnual Meeting of the Association for Computational Linguistics (ACL)Alejandro Hernández-Cano, Alexander Hägele, Allen Hao Huang, et al.2026 · 67 citations
  3. Toward Cross-Lingual Quality Classifiers for Multilingual Pretraining Data SelectionarXiv:2604.20549 · original ↗ · no summary yetYassine Turki et al.2026

 Co-authors here

11 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 148 times (of the 141,840 papers whose bibliographies we read); 19 citing papers with a page here →.

Updated

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