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# Adapting LLMs to Hebrew: Unveiling DictaLM 2.0 with Enhanced Vocabulary and Instruction Capabilities

**Authors:** [Shaltiel Shmidman](https://md.mova.elamur.ai/free/en/docs/papers/authors/shaltiel-shmidman/); [Avi Shmidman](https://md.mova.elamur.ai/free/en/docs/papers/authors/avi-shmidman/); [Amir DN Cohen](https://md.mova.elamur.ai/free/en/docs/papers/authors/amir-dn-cohen/); [Moshe Koppel](https://md.mova.elamur.ai/free/en/docs/papers/authors/moshe-koppel/) · **Year:** 2024 · **Venue:** arXiv:2407.07080 **Link:** [arxiv.org/abs/2407.07080](https://arxiv.org/abs/2407.07080) · library `arxivrecent-2024-shmidman-adapting-llms-hebrew-unveiling-dictalm` · **Licence:** [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)

[**0** citations](https://openalex.org/works?filter=cites:W4400519217) openalex-snapshot · 2026-10-14[**15** citations in our library](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/arxivrecent-2024-shmidman-adapting-llms-hebrew-unveiling-dictalm/) of the 140673 papers whose bibliographies we read[arXiv:2407.07080](https://openalex.org/S4306400194)[licence](http://creativecommons.org/licenses/by/4.0/)

- [Publisher's page](https://arxiv.org/abs/2407.07080)
- [PDF](https://arxiv.org/pdf/2407.07080)
- [arXiv 2407.07080](https://arxiv.org/abs/2407.07080)
- [OpenAlex](https://openalex.org/W4400519217)

**Authors (4)**

1. [Shaltiel Shmidman](https://openalex.org/A5041361841) h 3 · 27 citations · 21 works
2. [Avi Shmidman](https://openalex.org/A5013212409) h 5 · 114 citations · 53 works
3. [Amir DN Cohen](https://openalex.org/A5086960141) h 7 · 139 citations · 25 works
4. [Moshe Koppel](https://openalex.org/A5019908505) h 37 · 6949 citations · 150 works

Citations and author metrics from openalex-snapshot (2026-10-14); authors with the same name are not always told apart.

## What it is about

The paper adapts Mistral-7B to Hebrew by continued pre-training on about 200B tokens of half Hebrew and half English. Hebrew text comes from web crawls, social media, news, and books, filtered by Hebrew-character share and by a frequency test against a 1B-word gold corpus, then de-duplicated with MinHash at 0.8 Jaccard. The tokenizer gets 1,000 added Hebrew tokens, which halves the tokens per Hebrew word (from 5.81), and the new embeddings are first distilled from the old ones, then the LM head is calibrated. An instruct version is made by SFT on translated and native Hebrew dialogues, then DPO with a same-answer-different-language pair set to stop language switching. The evaluation uses few-shot Hebrew tasks (HeQ, sentiment, Winograd, BLEU translation) and a GPT-4 judge on 75 summaries, where DictaLM 2.0-Instruct after DPO scores below Claude 3 Haiku and GPT-4 but above Gemma-7B-it and close to Llama-3-8B-Instruct on consistency. The paper is a model report.

## Ideas

- I1. A deterministic Hebrew-content filter: a document is kept only if most of its words occur in a frequency list from a gold corpus, and non-Hebrew spans are replaced by one marker. The same two checks can screen `tools/langseg` output and the web text that feeds any Hebrew resource.
- I2. A Winograd-style pronoun test in Hebrew (the translated schema set) as a held-out check of pronoun resolution in [Nouns, adjectives, pronouns and numerals](https://md.mova.elamur.ai/free/en/docs/languages/he/04-nouns-adjectives-pronouns/); it needs commonsense, so it measures the limit of the rule-based world rather than the parser.
- I3. A language-match check on Mova's own answers: the reply must be in the language of the question, as the paper's pair set enforces for its model. The check is a rule on the output, measured on a set of mixed questions.

## For Mova

I1 goes into `tools/langseg` (a quality filter for Hebrew text) and into [2. Script and spelling](https://md.mova.elamur.ai/free/en/docs/languages/he/02-script-and-spelling/) as a note on frequency checks. I2 is a test set for [Nouns, adjectives, pronouns and numerals](https://md.mova.elamur.ai/free/en/docs/languages/he/04-nouns-adjectives-pronouns/). I3 is a check for the answer stage in `prag` and `world`, once Modern Hebrew input is supported ([12. Mova and Modern Hebrew: where it stands, what it would need](https://md.mova.elamur.ai/free/en/docs/languages/he/12-mova/)).

## Cite

The paper itself, not this card. BibTeX from the library's record of the work. [arXiv 2407.07080](https://arxiv.org/abs/2407.07080)

```
@misc{shmidman2024adapting,
  title = {{Adapting LLMs to Hebrew: Unveiling DictaLM 2.0 with Enhanced Vocabulary and Instruction Capabilities}},
  author = {Shmidman, Shaltiel and Shmidman, Avi and Cohen, Amir DN and Koppel, Moshe},
  year = {2024},
  eprint = {2407.07080},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2407.07080}
}
```

- [← previous The Knesset Corpus: An Annotated Corpus of Hebrew Parliamentary Proceedings](https://md.mova.elamur.ai/free/en/docs/papers/arxivrecent-2024-goldin-knesset-corpus-annotated-corpus-hebrew/)
- [next → TafBERTa: Learning Grammatical Rules from Small-Scale Language Acquisition Data in Hebrew](https://md.mova.elamur.ai/free/en/docs/papers/babylm-2025-gelboim-tafberta-learning-grammatical-rules-small/)
- [↑ back to Modern Hebrew](https://md.mova.elamur.ai/free/en/docs/papers/he/)

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