Michael Fromm
Michael Fromm has 2 papers in our library, in 2024–2026, most of them on Language models & pretraining, Multilingual & cross-lingual NLP, Tokenization & segmentation; often with Mehdi Ali, Abbas Goher Khan, Jan Ebert.
OpenAlex · h-index 3 · 15 works · 50 citations OpenAlex, 2026-10-14
📄 2 works in our library · 🔗 Cited 76 times in our library · ⭐ Paper Stars ranking: #6752
Affiliations
Fields, topics and concepts
📚 Shelves 4The field of science this work belongs to (OpenAlex fields).Artificial Intelligence · 2 worksArts and Humanities · 2 worksComputer Science · 2 worksLanguage and Linguistics · 2 works
🧭 Topics 3Research subjects and languages the work is about.Language models & pretraining · 2 worksMultilingual & 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
Papers
- Tokenizer Choice For LLM Training: Negligible or Crucial? (2024 · 104 citations)
Tokenizers are rarely examined when building LLMs. This paper asks whether the choice of tokenizer measurably affects decoder-only model quality and training cost, in both English-only and multilingual settings. The authors built two training corpora of 70B words each, with matched domain mixtures (80% web-crawled… - KletterMix: Climbing Toward High-Quality German Pretraining Data - The Full Report (2026 · 1 citation)
Open German pretraining data still trails English. Native German collections are usually smaller, noisier, less documented, and seldom tested by actually training models on them. The authors take a different route: instead of crawling more German web text, they machine-translate a strong English mixture, ClimbMix…
Co-authors here
- Mehdi Ali ×2
- Abbas Goher Khan
- Jan Ebert
- Johannes Leveling
- Klaudia Thellmann
- Kristian Kersting
- Maurice Kraus
- Nicolas Flores-Herr
- Richard Rutmann
- Ruben Harle
- Sebastian Sztwiertnia
Venues
- Findings of the Association for Computational Linguistics: Findings of the Association for Computational Linguistics 2024
Cited by
Updated
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