[Mova](https://md.mova.elamur.ai/free/en/) · [this page in HTML](https://mova.elamur.ai/free/en/docs/papers/cited-by/hbo-2022-gueta-large-pre-trained-models-extra/) [home](https://md.mova.elamur.ai/free/en/) / [docs](https://md.mova.elamur.ai/free/en/docs/) / [papers](https://md.mova.elamur.ai/free/en/docs/papers/) / [cited-by](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/) / Who cites «Large Pre-Trained Models with Extra-Large Vocabularies: A Contrastive Analysis of Hebrew BERT Models and a New One to Outperform Them All» # Who cites «Large Pre-Trained Models with Extra-Large Vocabularies: A Contrastive Analysis of Hebrew BERT Models and a New One to Outperform Them All» 23 papers of our library cite it, newest first: found in their bibliographies, which we read ourselves. [← the card](https://md.mova.elamur.ai/free/en/docs/papers/hbo-2022-gueta-large-pre-trained-models-extra/) 1. [A New State-of-the-Art BERT Model for Judeo-Arabic](https://aclanthology.org/2026.lt4hala-1.6/) *— 2026 · Proceedings of the Fourth Workshop on Language Technologies for Historical and Ancient Languages (LT4HALA 2026) @ LREC 2026* 2. [Annotation Matters: Resolving Cross-Corpus Performance Drops in Hebrew Offensive Language Detection](https://md.mova.elamur.ai/free/en/docs/papers/aclbulk-2026-hefetz-annotation-matters-resolving-cross-corpus/) *— our card · 2026* 3. [HalleluBERT: Let Every Token That Has Meaning Bear Its Weight](https://md.mova.elamur.ai/free/en/docs/papers/lingbulk-2026-schmitt-hallelubert-let-every-token-that/) *— our card · 2026 · Proceedings of the Fifteenth Language Resources and Evaluation Conference* 4. [Hebrew Diacritics Restoration using Visual Representation](https://md.mova.elamur.ai/free/en/docs/papers/aclbulk-2026-elboher-hebrew-diacritics-restoration-visual-representation/) *— our card · 2026 · Proceedings of the Second Workshop on Language Models for Low-Resource Languages (LoResLM 2026)* 5. [A Classifier of Word-Level Variants in Witnesses of Biblical Hebrew Manuscripts](https://md.mova.elamur.ai/free/en/docs/papers/findings-2025-stoupak-classifier-word-level-variants-witnesses/) *— our card · 2025 · Findings of the Association for Computational Linguistics: ACL 2025* 6. [LCHAIM - Investigating Long Context Reasoning in Hebrew](https://md.mova.elamur.ai/free/en/docs/papers/findings-2025-malul-lchaim-investigating-long-context-reasoning/) *— our card · 2025* 7. [NeoDictaBERT: Pushing the Frontier of BERT models for Hebrew](https://md.mova.elamur.ai/free/en/docs/papers/langstream-2025-shmidman-neodictabert-pushing-frontier-bert-models/) *— our card · 2025 · arXiv:2510.20386* 8. [Restoring Missing Spaces in Scraped Hebrew Social Media](https://md.mova.elamur.ai/free/en/docs/papers/aclbulk-2025-shmidman-restoring-missing-spaces-scraped-hebrew/) *— our card · 2025* 9. [Systematic Textual Availability of Manuscripts](https://md.mova.elamur.ai/free/en/docs/papers/aclbulk-2025-miller-systematic-textual-availability-manuscripts/) *— our card · 2025* 10. [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/) *— our card · 2025 · Proceedings of the First BabyLM Workshop* 11. [Embible: Reconstruction of Ancient Hebrew and Aramaic Texts Using Transformers](https://md.mova.elamur.ai/free/en/docs/papers/aclbulk-2024-fono-embible-reconstruction-ancient-hebrew-aramaic/) *— our card · 2024 · Findings of the Association for Computational Linguistics: EACL 2024* 12. [MsBERT: A New Model for the Reconstruction of Lacunae in Hebrew Manuscripts](https://md.mova.elamur.ai/free/en/docs/papers/aclbulk-2024-shmidman-msbert-new-model-reconstruction-lacunae/) *— our card · 2024 · Proceedings of the 1st Workshop on Machine Learning for Ancient Languages (ML4AL 2024)* 13. [OtoBERT: Identifying Suffixed Verbal Forms in Modern Hebrew Literature](https://md.mova.elamur.ai/free/en/docs/papers/aclbulk-2024-shmidman-otobert-identifying-suffixed-verbal-forms/) *— our card · 2024* 14. [Unpacking Tokenization: Evaluating Text Compression and its Correlation with Model Performance](https://aclanthology.org/2024.findings-acl.134/) *— 2024 · Findings of the Association for Computational Linguistics: ACL 2024* 15. [DictaBERT: A State-of-the-Art BERT Suite for Modern Hebrew](https://md.mova.elamur.ai/free/en/docs/papers/arxivrecent-2023-shmidman-dictabert-state-art-bert-suite/) *— our card · 2023 · arXiv:2308.16687* 16. [Explicit Morphological Knowledge Improves Pre-training of Language Models for Hebrew](https://md.mova.elamur.ai/free/en/docs/papers/langstream-2023-gueta-explicit-morphological-knowledge-improves-pre/) *— our card · 2023 · arXiv:2311.00658* 17. [HeQ: a Large and Diverse Hebrew Reading Comprehension Benchmark](https://md.mova.elamur.ai/free/en/docs/papers/findings-2023-cohen-heq-large-diverse-hebrew-reading/) *— our card · 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023* 18. [HeRo: RoBERTa and Longformer Hebrew Language Models](https://md.mova.elamur.ai/free/en/docs/papers/langstream-2023-shalumov-hero-roberta-longformer-hebrew-language/) *— our card · 2023 · arXiv:2304.11077* 19. [Multilingual Sequence-to-Sequence Models for Hebrew NLP](https://md.mova.elamur.ai/free/en/docs/papers/findings-2023-eyal-multilingual-sequence-sequence-models-hebrew/) *— our card · 2023 · Findings of the Association for Computational Linguistics: ACL 2023* 20. [Temporal Relation Classification in Hebrew](https://md.mova.elamur.ai/free/en/docs/papers/narr-2023-yanko-temporal-relation-classification-hebrew/) *— our card · 2023 · Findings of the Association for Computational Linguistics: IJCNLP-AACL 2023 (Findings)* 21. [ivrit.ai: A Comprehensive Dataset of Hebrew Speech for AI Research and Development](https://md.mova.elamur.ai/free/en/docs/papers/langstream-2023-marmor-ivrit-ai-comprehensive-dataset-hebrew/) *— our card · 2023 · arXiv:2307.08720* 22. goldin-2025-annotation-scheme-factuality-its-application 23. levy-2023-is-probing-all-you-need - [← previous Who cites «Clustering Biblical Texts Using Recurrent Neural Networks»](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/hbo-2020-schans-clustering-biblical-texts-using-recurrent/) - [next → Who cites «Introducing BEREL: BERT Embeddings for Rabbinic-Encoded Language»](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/hbo-2022-shmidman-introducing-berel-bert-embeddings-rabbinic/) - [↑ back to papers/cited-by/](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/)