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MameLoshnLM: Yiddish Language Model and Evaluation Benchmark

Paper · 2026 · Uri Katz, Omer Goldman, Tomasz Limisiewicz et al.

Summary

Yiddish has a long written tradition but a small and unreliable presence on the web, so multilingual models handle it poorly and there are few trustworthy ways to measure them. The authors first audit the Yiddish split of mC4 and find that most of it is not authentic Yiddish: much is machine-translated spam or Hebrew that was misclassified. In response they build Oytser, a curated pretraining corpus of about 915M words. It joins contemporary web sources (news, Wikipedia, forums, a Bible translation) with a large collection of OCRed books from the Yiddish Book Center. They also assemble Kashes, a multi-task benchmark covering translation, linguistic analysis, information extraction and understanding. Continued pretraining of Llama 3.1 8B on this data yields MameLoshnLM, which beats open models of comparable size across Kashes. Its output is also more natural Yiddish, with fewer Germanized or translationese features. The broader lesson is that for languages like this, the main obstacle is noisy web data that does not match real usage, not only the small amount of data.

Key points

  • mC4 audit. Only 42.2% of Yiddish-tagged mC4 pages come from known genuine Yiddish sources. At least 29.8% look machine-translated, 21.9% are misidentified Hebrew, and about 6.2% are miscellaneous.
  • Oytser size and largest source. The corpus has 346.3K documents, 915.21M words and 5.28B tokens from 9 sources. The YBC library (OCRed with Jochre 3) dominates, with 12.3K books and about 723M words.
  • Web-native sources. These include the forums Ivelt and Kaveshtiebel, the Forward, Yiddish Wikipedia, yiddish.news, Hamaspik, Lebns Fragn and the Hebrew Bible.
  • Training data mix. Including English data during continued pretraining helps preserve the base model's general abilities. Adding historically or genealogically related languages helps only a little and inconsistently.
  • Release terms and licensing. The model is released under a non-commercial license. Over 82% of training tokens come from licensed, open or public-domain sources, and forum text was stripped of account and thread structure for privacy.

Interesting ideas

  • I1. Auditing a corpus before trusting its label. The authors check source URLs for auto-generated per-language site copies, run a second language identifier over the whole split, and manually review domains, which shows how much of a supposedly valid split is fake.
  • I2. Scoring naturalness, not only accuracy. Generated text is compared for language-defining lexical and morphological patterns, so a model can be caught producing translationese or text skewed toward a related language even when task scores look acceptable.
  • I3. Replaying original data during adaptation. Mixing English back in during continued pretraining protects existing abilities, while adding loosely related data brought little benefit.
  • I4. Benchmarks native to the target, plus practitioner datasets. Kashes favors tasks designed for Yiddish over machine-translated ones, and includes NER sets that digital humanities scholars built for their own work, so gains reflect real usefulness.

For Mova

How these ideas could serve Mova, our small open language model — its files, stages and tests:

  • I1 (learn): Run the same kind of audit on silver-data sources before witness parsers vote, flagging machine-translated or templated pages that would otherwise contaminate training.
  • I2 (back): Add a naturalness check to generation and the text → graph → text compressor that flags calque-like or unnatural English, alongside meaning-preservation metrics.
  • I3 (learn): When retraining gparse or the perceptron parser on a new domain or dialect, keep replaying the original treebank data, and test whether extra "related" data actually helps before adding it.
  • I4 (answer): Build reader tests from naturally written school and fable material rather than translated or synthetic items, and include tasks that real users actually need solved.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

 Institutions

🎓University of WashingtonSeattle · United States402 works · 212,762 citations🔬Allen Institute for Artificial IntelligenceSeattle · United States304 works · 106,138 citations🎓University of CambridgeCambridge · United Kingdom256 works · 123,373 citations🎓Bar-Ilan UniversityRamat Gan · Israel146 works · 24,845 citations

 Fields, topics and concepts

📚 Shelves 4The field of science this work belongs to (OpenAlex fields).Computer Science · 18,614 worksArtificial Intelligence · 17,965 worksArts and Humanities · 17,450 worksLanguage and Linguistics · 17,296 works

🧭 Topics 4Research subjects and languages the work is about.Language models & pretraining · 3,894 worksSemitic languages · 3,066 worksEvaluation & benchmarks · 1,341 worksHebrew & Aramaic · 496 works

Cite

Cite as Uri Katz, Omer Goldman, Tomasz Limisiewicz, Reut Tsarfaty and Noah A. Smith. 2026. MameLoshnLM: Yiddish Language Model and Evaluation Benchmark. arXiv:2608.05850 · Proceedings of the Third Conference on Language Modeling (COLM 2026), 2026.

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.48550/arxiv.2608.05850 · arXiv 2608.05850

@misc{katz2026mameloshnlm,
  title = {{MameLoshnLM: Yiddish Language Model and Evaluation Benchmark}},
  author = {Uri Katz, Omer Goldman, Tomasz Limisiewicz, Reut Tsarfaty and Noah A. Smith},
  year = {2026},
  doi = {10.48550/arxiv.2608.05850},
  eprint = {2608.05850},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.2608.05850}
}

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