---
title: "Who cites Canine: Pre-training an Efficient…"
description: "156 papers of the Elamur Research library cite «Canine: Pre-training an Efficient Tokenization-Free Encoder for Language Representation», found in their…"
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# Who cites «Canine: Pre-training an Efficient Tokenization-Free Encoder for Language Representation»

156 papers of our library cite it (of the 140,884 whose bibliographies we read) · 13 with a page here, the most cited first · [← the paper](https://md.research.elamur.ai/free/en/papers/clark-2022-canine-pre-training-efficient-tokenization/)

1. [**How Good is Your Tokenizer? On the Monolingual Performance of Multilingual Language Models**Phillip Rust, Jonas Pfeiffer, Ivan Vulić, et al.*2021 · 444 citations*](https://md.research.elamur.ai/free/en/papers/rust-2021-how-good-is-your-tokenizer/)
2. [**Challenges and Applications of Large Language Models**Jean Kaddour, Joshua Harris, Maximilian Mozes, et al.*2023 · 300 citations*](https://md.research.elamur.ai/free/en/papers/kaddour-2023-challenges-applications-large-language-models/)
3. [**XTREME-R: Towards More Challenging and Nuanced Multilingual Evaluation**Sebastian Ruder, Noah Constant, Jan Botha, et al.*2021 · 277 citations*](https://md.research.elamur.ai/free/en/papers/ruder-2021-xtreme-r-more-challenging-nuanced/)
4. [**Charformer: Fast Character Transformers via Gradient-based Subword Tokenization**Yi Tay, Vinh Q. Tran, Sebastian Ruder, et al.*2021 · 179 citations*](https://md.research.elamur.ai/free/en/papers/tay-2021-charformer-fast-character-transformers-gradient/)
5. [**Lifting the Curse of Multilinguality by Pre-training Modular Transformers**Jonas Pfeiffer, Naman Goyal, Xi Victoria Lin, et al.*2022 · 172 citations*](https://md.research.elamur.ai/free/en/papers/pfeiffer-2022-lifting-curse-multilinguality-pre-training/)
6. [**Language Model Tokenizers Introduce Unfairness Between Languages**Aleksandar Petrov, Emanuele La Malfa, Philip H. S. Torr, et al.*2023 · 149 citations*](https://md.research.elamur.ai/free/en/papers/petrov-2023-language-model-tokenizers-introduce-unfairness/)
7. [**Fanar: An Arabic-Centric Multimodal Generative AI Platform**Fanar Team: Ummar Abbas, Mohammad Shahmeer Ahmad, Firoj Alam, et al.*2025 · 127 citations*](https://md.research.elamur.ai/free/en/papers/abbas-2025-fanar-arabic-centric-multimodal-generative/)
8. [**Tokenizer Choice For LLM Training: Negligible or Crucial?**Mehdi Ali, Michael Fromm, Klaudia Thellmann, et al.*2024 · 102 citations*](https://md.research.elamur.ai/free/en/papers/ali-2024-tokenizer-choice-llm-training-negligible/)
9. [**Between words and characters: A Brief History of Open-Vocabulary Modeling and Tokenization in NLP**Sabrina J. Mielke, Zaid Alyafeai, Elizabeth Salesky, et al.*2021 · 89 citations*](https://md.research.elamur.ai/free/en/papers/mielke-2021-between-words-characters-brief-history/)
10. [**Revisiting Pre-trained Language Models and their Evaluation for Arabic Natural Language Understanding**Abbas Ghaddar, Yimeng Wu, Sunyam Bagga, et al.*2022 · 9 citations*](https://md.research.elamur.ai/free/en/papers/ghaddar-2022-revisiting-pre-trained-language-models-their/)
11. [**Explicit Morphological Knowledge Improves Pre-training of Language Models for Hebrew**Eylon Gueta, Omer Goldman, Reut Tsarfaty*2023 · 7 citations*](https://md.research.elamur.ai/free/en/papers/gueta-2023-explicit-morphological-knowledge-improves-pre/)
12. [**Expanding the Lexicon of Ge'ez Based African Languages: A Comparative Study of Amharic and Tigrinya**Hailay Kidu Teklehaymanot, Debela Desalegn Yadeta, Wolfgang Nejdl*2026*](https://md.research.elamur.ai/free/en/papers/teklehaymanot-2026-expanding-lexicon-ge-ez-based/)
13. [**The Impact of Tokenization Algorithms on Hungarian Language Model Performance**Mátyás Osváth, Máté Norbert Molnár, Roland Gunics, et al.*2026*](https://md.research.elamur.ai/free/en/papers/osvath-2026-impact-tokenization-algorithms-hungarian-language/)

143 more papers of our library cite it; they have no page here yet.

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