How Good is Your Tokenizer? On the Monolingual Performance of Multilingual Language Models
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Phillip Rust, Jonas Pfeiffer, Ivan Vulić, Sebastian Ruder, Iryna Gurevych. Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). 2021.
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High fertility increases inference cost and compresses effective context windows.
“increasing inference cost and compressing effective context windows (Rust et al., 2021; Petrov et al., 2024).”
From Writing-System-Level Tokenizer Adaptation for Byte-Level BPE (2026) · cites this paper as Rust et al., 2021
Section 4.2 (page not provided) defines high fertility as producing more subwords per word, and Appendix B.1 reports longer tokenized sentences for higher-fertility tokenizers. That supports the basis for fewer words fitting within a fixed token limit, but the paper does not address inference cost or explicitly assess effective context windows. (LLM, conf=0.77)
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Şahin, G. G. (2021) · Computational Linguistics · doi:10.1162/coli_a_00425 · Cited by 49 · see citing works
Smadi, B. (2024) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2024.107921 · Cited by 48 · see citing works
A Representative Study on Human Detection of Artificially Generated Media Across Countries
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A Comprehensive Analysis of Various Tokenizers for Arabic Large Language Models
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Self-citationBERT for Natural Language Processing in Bahasa Indonesia
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Tan, Y., Li, M., Zhou, Z., Tan, P., Yu, H., Fan, G., & Liang, H. (2024) · Journal of Cheminformatics · doi:10.1186/s13321-024-00884-3 · Cited by 21 · see citing works
Facilitating Large Language Model Russian Adaptation with Learned Embedding Propagation
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Deep Lexical Hypothesis: Identifying personality structure in natural languagePreprint
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Thin, D. V., Hao, D. N., & Nguyen, N. L.-T. (2023) · ACM Transactions on Asian and Low-Resource Language Information Processing · doi:10.1145/3589131 · Cited by 18 · see citing works
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Kaushal, A., & Mahowald, K. (2022) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/2022.naacl-main.179 · Cited by 16 · see citing works
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Impact of Tokenization on LLaMa Russian Adaptation
Tikhomirov, M., & Chernyshev, D. (2023) · Ivannikov ISPRAS Open Conference (ISPRAS) · doi:10.1109/ispras60948.2023.10508177 · Cited by 15 · see citing works
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Chau, E. C., & Smith, N. A. (2021) · Workshop on Multilingual Representation Learning (MRL) · doi:10.18653/v1/2021.mrl-1.5 · Cited by 15 · see citing works
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Self-citationModelling Latent Translations for Cross-Lingual TransferPreprint
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Self-citationChang, T. A., Tu, Z., & Bergen, B. K. (2024) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00708 · Cited by 13 · see citing works
Micallef, K., Albert Gatt, A., Tanti, M., & van der Plas, L. (2022) · Workshop on Deep Learning Approaches for Low-Resource NLP (DeepLo) · doi:10.18653/v1/2022.deeplo-1.10 · Cited by 13 · see citing works
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Bhattacharjee, A., Hasan, T., Samin, K., Islam, S., Rahman, M. S., Iqbal, A., & Shahriyar, R. (2021) · arXiv (Cornell University) · Cited by 12 · see citing works
Named Entity Recognition for Low-Resource Languages - Profiting from Language Families
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Hu, S., Zhou, H., Hergul, M., Gritta, M., Zhang, G., Iacobacci, I., Vulić, I., & Korhonen, A. (2023) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00609 · Cited by 11 · see citing works
Self-citationUnveiling Challenges for LLMs in Enterprise Data Engineering
Bodensohn, J.-M., Brackmann, U., Vogel, L., Sanghi, A., & Binnig, C. (2025) · Proceedings of the VLDB Endowment · doi:10.14778/3773749.3773758 · Cited by 11 · see citing works
Limisiewicz, T., Balhar, J., & Mareček, D. (2023) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2023.findings-acl.350 · Cited by 11 · see citing works
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MultiFin: A Dataset for Multilingual Financial NLP
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Gao, S., Zhao, X., Chen, L., Yu, J., Tian, S., Zhou, H., Chen, J., Long, S., He, Q., & Feng, X. (2025) · npj Digital Medicine · doi:10.1038/s41746-025-02139-3 · Cited by 10 · see citing works
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Scene-Text Aware Image and Text Retrieval with Dual-Encoder
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FOCUS: Effective Embedding Initialization for Monolingual Specialization of Multilingual Models
Dobler, K., & de Melo, G. (2023) · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2023.emnlp-main.829 · Cited by 8 · see citing works
gaBERT — an Irish Language Model
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Causal and Masked Language Modeling of Javanese Language using Transformer-based Architectures
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MergeDistill: Merging Language Models using Pre-trained Distillation
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Energy-Aware Multilingual Evaluation of Large Language Models
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Predictability and Causality in Spanish and English Natural Language Generation
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Are Multilingual Sentiment Models Equally Right for the Right Reasons?
Jørgensen, R. L., Caccavale, F., Igel, C., & Søgaard, A. (2022) · BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP (BlackboxNLP) · doi:10.18653/v1/2022.blackboxnlp-1.11 · Cited by 6 · see citing works
Analyzing Cognitive Plausibility of Subword Tokenization
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- Authors
- Rust, P., Pfeiffer, J., Vulić, I., Ruder, S., & Gurevych, I.
- Affiliations
- Technische Universität Darmstadt (DE)
- University of Cambridge (GB)
- Venue
- Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)
- Published
- 2021
- Pages
- 3118-3135
- OpenAlex
- W3120253119
- Cited by
- 202
- References
- 57
- Citations by year
- 2026: 32 · 2025: 32 · 2024: 22 · 2023: 50 · 2022: 35 · 2021: 31
- FWCI
- 15.94
- Type
- conference paper
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- arXiv (Cornell University) (submittedVersion)
- TUbilio (Technical University of Darmstadt) (submittedVersion, cc-by-nc-sa)
- arXiv (Cornell University)
- Funders
- H2020 European Research Council (648909)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Artificial Intelligence › Speech Recognition and Synthesis
- Keywords
- tokenization, multilingual language models, multilingual tokenization, language model evaluation
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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