arXiv:2607. 23319v1 Announce Type: cross Abstract: Standard subword tokenization algorithms such as Byte-Pair Encoding (BPE) and SentencePiece are trained predominantly on modern language corpora and produce inefficient segmentations when applied to classical Indian languages.
By Poornima Kumaresan, Pavithra Muruganantham, Lakshmi Rajendran, Santhosh Sivasubramani
The study investigates whether morphological correctness influences tokenization effectiveness in Tagalog, a language with infix morphology that disrupts contiguous roots. Researchers developed MorphAustro‑Tok, a tokenizer that preserves the root as a contiguous unit and separates infixes, and compared it to a standard BPE baseline and a scrambled‑morpheme control that randomizes infix placement. Across multiple runs, MorphAustro‑Tok consistently outperformed the baseline, but the scrambled control showed no significant difference, indicating that morphological correctness does not contribute to the observed gains at this scale.
By Abdul Rehman Antall, Baidya Nath Saha
arXiv:2607.15209v4 Announce Type: replace
Abstract: Multilingual pre-trained language models such as XLM-R perform well for major languages but struggle with low-resource Ge'ez-script languages, larg...
By Hailay Kidu Teklehaymanot, Debela Desalegn Yadeta, Wolfgang Nejdl
arXiv:2506. 15138v2 Announce Type: replace-cross Abstract: Tokenization directly affects the inference efficiency of large language models, since fragmented tokenization increases sequence length and generation cost.
By Gyeongje Cho, Yeonkyoung So, Sangmin Lee, Jaejin Lee
arXiv:2609.15991v1 Announce Type: new
Abstract: Standard subword tokenizers either treat every orthographic variation of a word (such as hello, Hello, HELLO, and H\'ello) as unrelated vocabulary entr...
By Connor Makowski, Willem Guter
arXiv:2606. 18717v1 Announce Type: cross Abstract: Turkish is agglutinative: meaning is carried by morphemes, yet the subword tokenizers that drive modern language models split words by corpus statistics, fragmenting semantically loaded suffixes and -- in the case of WordPiece and rule-based analyzers -- failing to decode their output back to the original text.
By Tolga \c{S}akar