--- title: "Who cites Byte Pair Encoding is Suboptimal for Language…" description: "136 papers of the Elamur Research library cite «Byte Pair Encoding is Suboptimal for Language Model Pretraining», found in their bibliographies; the most…" canonical: "https://research.elamur.ai/free/en/papers/bostrom-2020-byte-pair-encoding-is-suboptimal/cited-by/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/bostrom-2020-byte-pair-encoding-is-suboptimal/cited-by/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [Byte Pair Encoding is Suboptimal for Language…](https://md.research.elamur.ai/free/en/papers/bostrom-2020-byte-pair-encoding-is-suboptimal/) / cited by # Who cites «Byte Pair Encoding is Suboptimal for Language Model Pretraining» 136 papers of our library cite it (of the 140,884 whose bibliographies we read) · 20 with a page here, the most cited first · [← the paper](https://md.research.elamur.ai/free/en/papers/bostrom-2020-byte-pair-encoding-is-suboptimal/) 1. [**BloombergGPT: A Large Language Model for Finance**Shijie Wu, Ozan Irsoy, Steven Lu, et al.*2023 · 608 citations*](https://md.research.elamur.ai/free/en/papers/wu-2023-bloomberggpt-large-language-model-finance/) 2. [**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/) 3. [**Do All Languages Cost the Same? Tokenization in the Era of Commercial Language Models**Orevaoghene Ahia, Sachin Kumar, Hila Gonen, et al.*2023 · 164 citations*](https://md.research.elamur.ai/free/en/papers/ahia-2023-do-all-languages-cost-same/) 4. [**Canine: Pre-training an Efficient Tokenization-Free Encoder for Language Representation**Jonathan H. Clark, Dan Garrette, Iulia Turc, et al.*2022 · 156 citations*](https://md.research.elamur.ai/free/en/papers/clark-2022-canine-pre-training-efficient-tokenization/) 5. [**Superbizarre Is Not Superb: Derivational Morphology Improves BERT's Interpretation of Complex Words**Valentin, Hofmann*2021 · 101 citations*](https://md.research.elamur.ai/free/en/papers/hofmann-2021-superbizarre-derivational/) 6. [**Tokenization Is More Than Compression**Craig W Schmidt, Varshini Reddy, Haoran Zhang, et al.*2024 · 66 citations*](https://md.research.elamur.ai/free/en/papers/schmidt-2024-tokenization-is-more-than-compression/) 7. [**Evaluating Various Tokenizers for Arabic Text Classification**Zaid Alyafeai, Maged S. Al-shaibani, Mustafa Ghaleb, et al.*2021 · 66 citations*](https://md.research.elamur.ai/free/en/papers/alyafeai-2021-evaluating-various-tokenizers-arabic-text/) 8. [**Tokenization counts: the impact of tokenization on arithmetic in frontier LLMs**Aaditya K. Singh, DJ Strouse*2024 · 53 citations*](https://md.research.elamur.ai/free/en/papers/singh-2024-tokenization-counts-impact-tokenization-arithmetic/) 9. [**Counting the Bugs in ChatGPT's Wugs: A Multilingual Investigation into the Morphological Capabilities of a Large Language Model**Leonie Weissweiler, Valentin Hofmann, Anjali Kantharuban*2023 · 47 citations*](https://md.research.elamur.ai/free/en/papers/weissweiler-2023-chatgpt-wugs-morphology/) 10. [**Prediction of Arabic Legal Rulings using Large Language Models**Adel Ammar, Anis Koubaa, Bilel Benjdira, et al.*2023 · 27 citations*](https://md.research.elamur.ai/free/en/papers/ammar-2023-prediction-arabic-legal-rulings-using/) 11. [**The Impact of Token Granularity on the Predictive Power of Language Model Surprisal**Byung-Doh Oh, William Schuler*2025 · 12 citations*](https://md.research.elamur.ai/free/en/papers/oh-2025-impact-token-granularity-predictive-power/) 12. [**Exploring Tokenization Strategies and Vocabulary Sizes for Enhanced Arabic Language Models**Mohamed Taher Alrefaie, Nour Eldin Morsy, Nada Samir*2024 · 5 citations*](https://md.research.elamur.ai/free/en/papers/alrefaie-2024-exploring-tokenization-strategies-vocabulary-sizes/) 13. [**Kuwain 1.5B: An Arabic SLM via Language Injection**Khalil Hennara, Sara Chrouf, Mohamed Motaism Hamed, et al.*2025 · 4 citations*](https://md.research.elamur.ai/free/en/papers/hennara-2025-kuwain-1-5b-arabic-slm/) 14. [**MoVoC: Morphology-Aware Subword Construction for Ge'ez Script Languages**Hailay Kidu Teklehaymanot, Dren Fazlija, Wolfgang Nejdl*2025 · 4 citations*](https://md.research.elamur.ai/free/en/papers/teklehaymanot-2025-movoc-morphology-aware-subword-construction/) 15. [**MoVoC: Morphology-Aware Subword Construction for Geez Script Languages**Hailay Kidu Teklehaymanot, Dren Fazlija, Wolfgang Nejdl*2025 · 4 citations*](https://md.research.elamur.ai/free/en/papers/teklehaymanot-2025-movoc-morphology-aware-subword-construction-geez/) 16. [**Do Diacritics Matter? Evaluating the Impact of Arabic Diacritics on Tokenization and LLM Benchmarks**Go Inoue, Bashar Alhafni, Nizar Habash, et al.*2026 · 3 citations*](https://md.research.elamur.ai/free/en/papers/inoue-2026-do-diacritics-matter-evaluating-impact/) 17. [**Morphemes Without Borders: Evaluating Root-Pattern Morphology in Arabic Tokenizers and LLMs**Yara Alakeel, Chatrine Qwaider, Hanan Aldarmaki, et al.*2026 · 1 citation*](https://md.research.elamur.ai/free/en/papers/alakeel-2026-morphemes-borders-evaluating-root-pattern/) 18. [**Morphemes without Borders: Evaluating Root–Pattern Morphology in Arabic Tokenizers and LLMs**Yara Yousif Alakeel, Chatrine Qwaider, Hanan Aldarmaki, et al.*2026 · 1 citation*](https://md.research.elamur.ai/free/en/papers/alakeel-2026-morphemes-without-borders-evaluating-root/) 19. [**AI Corpus Linguist: More than a Year of Experience**Jiří Milička, Tomáš Machálek*2026*](https://md.research.elamur.ai/free/en/papers/milicka-2026-ai-corpus-linguist-more-than/) 20. [**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/) 116 more papers of our library cite it; they have no page here yet. - [← back to](https://md.research.elamur.ai/free/en/papers/bostrom-2020-byte-pair-encoding-is-suboptimal/) — Byte Pair Encoding is Suboptimal for Language Model Pretraining