SuTRA (Structurally-Unified Tokenization with Root Awareness) is a morphology-aware tokenization algorithm designed to address the problem of Morphological Shattering in morphologically rich Indic languages. It preserves the indivisibility of aksharas—complex orthographic syllables—by penalizing merges that cross morphological boundaries, thereby reducing over-fragmentation of words. The authors also release a new morphological segmentation dataset for Hindi, Marathi, and Gujarati, and demonstrate that SuTRA improves morphological alignment by up to 14.7% and semantic recoverability by 34% over BPE, leading to an average machine translation gain of +8.08 chrF2.
By Vaibhav Rathore, Siddhant Gole, Dadhichi Telwadkar, Rooshil Bhatia, Maulik Ruparel, Siddharth Surekha, Neha Bhargava
arXiv:2608. 01935v1 Announce Type: cross Abstract: Prior work in Ancient Greek NLP relies on corpora that do not disambiguate the phonemic vowel length of alpha, iota, and ypsilon, together known as the dichrona.
By Albin Th\"orn Cleland, Eric Cullhed
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 paper shows that HuggingFace’s ByteLevel pre‑tokenizer, which treats a word as a sequence of Unicode letters, splits abugida scripts at every vowel sign, creating a training‑free lower bound on tokenizer fertility. Across 26 languages, all 17 abugidas exhibit increased token counts (up to 9×), while Latin, Cyrillic, Hangul, and Han remain unchanged. The authors demonstrate that correcting the character class reduces Nepali token counts, improves model performance, and that this issue is widespread in popular HuggingFace models.
By Sajal Regmi, Siddhartha Pudasaini, Chetan Phakami Pun
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
The paper introduces two new tokenisation algorithms—BottomUpLL and TopDownComp—to systematically explore the 2x2 design space defined by optimisation objective (compression vs. log‑likelihood) and search procedure (bottom‑up merging vs. top‑down pruning). Experiments across model sizes, vocabularies, and domains show that the search procedure, rather than the objective, consistently yields lower bits‑per‑byte, while no clear pattern emerges on the BLiMP benchmark. These findings clarify how tokeniser design choices influence language‑model performance and provide guidance for constructing tokenisers more principledly.
By Ahmetcan Yavuz, Clara Meister, Tiago Pimentel
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:2603. 26292v2 Announce Type: replace-cross Abstract: Syllable-level units offer compact and linguistically meaningful representations for spoken language modeling and unsupervised word discovery, but research on syllabification remains fragmented across disparate implementations, datasets, and evaluation protocols.
By H\'ector Javier V\'azquez Mart\'inez
arXiv:2606. 15144v1 Announce Type: cross Abstract: Large language models (LLMs) process text as sequences of subword tokens, which can obscure the character-level and morphological structure that underlies word formation.
By Jann Railey Montalan, David Demitri Africa, Jimson Paulo Layacan, Richell Isaiah Flores, Ivan Yuri De Leon, Lance Calvin Gamboa
CWoMP (Contrastive Word‑Morpheme Pretraining) is a new approach for generating interlinear glossed text that treats morphemes as atomic form‑meaning units with learned representations. It uses a contrastively trained encoder to align words in context with their constituent morphemes in a shared embedding space, and an autoregressive decoder that retrieves morpheme sequences from a mutable lexicon of these embeddings. The method yields interpretable predictions grounded in lexicon entries and allows users to improve results at inference time by expanding the lexicon without retraining, achieving superior performance and efficiency on diverse low‑resource languages, especially in extremely low‑resource settings.
By Morris Alper, Enora Rice, Bhargav Shandilya, Alexis Palmer, Lori Levin
The paper introduces UniLID, a lightweight language identification method that uses the UnigramLM tokenization algorithm to predict a string’s language by evaluating which language’s unigram distribution best explains the text. UniLID is data‑ and compute‑efficient, allows incremental addition of new languages without retraining, and can be integrated into existing tokenization pipelines. Experiments show competitive performance against baselines such as fasttext, GlotLID‑M, and CLD3, achieving 69% accuracy with five labeled samples per language and 89% with 25, and delivering significant gains on fine‑grained dialect identification.
By Clara Meister, Ahmetcan Yavuz, Pietro Lesci, Tiago Pimentel
arXiv:2606. 24172v1 Announce Type: cross Abstract: More than a billion people communicate in Indic languages, yet the natural language processing infrastructure serving them remains fragmented and underdeveloped.
By Ritwik Banerjee, Lav R. Varshney