arXiv:2611.05803v1 Announce Type: new
Abstract: How much phonological knowledge do language models (LMs) possess about Chinese characters, and how is this knowledge represented and used? We introduce...
By Xiaozhe Ji, Renfen Hu, Hai Hu
The study evaluates phonologically informed tokenization for German speech recognition using a wav2vec 2.0 backbone fine‑tuned with CTC. Three tokenizer families—pretrained multilingual characters, data‑driven BPE, and phonologically informed units from syllabification and grapheme‑to‑phoneme conversion—are compared across 40 fine‑tunes on three German test sets (in‑domain read, dialectal spontaneous, and standard spontaneous speech). Results show that phonologically informed tokenizers match BPE and character baselines in‑domain, but under domain shift their performance depends on vocabulary size rather than linguistic variation, with syllable‑aware tokenization benefiting dialectal and spontaneous speech at small vocabularies; a phoneme‑level confusion analysis indicates that the acoustic encoder dominates error patterns.
By Christopher Witzl, Tobias Bocklet, Korbinian Riedhammer
The paper introduces TokenAdapt, a model‑agnostic tokenizer transplantation method that uses a hybrid heuristic to initialize new token embeddings, and a novel pre‑tokenization learning approach for multi‑word Supertokens to improve compression. TokenAdapt combines local subword decomposition and global semantic similarity to preserve semantics while reducing retraining needs. Empirical results show that TokenAdapt outperforms existing baselines such as Transtokenizer and ReTok, achieving lower perplexity ratios and significant compression gains.
By Shaurya Sharthak, Vinayak Pahalwan, Adithya Kamath, Adarsh Shirawalmath
arXiv:2606. 15521v1 Announce Type: cross Abstract: Tokenization introduces representational redundancy: under a fixed token vocabulary, every byte string admits many valid token encodings, or segmentations, that decode to the same surface string.
By Kanishk Jain, Matthew Day, Tankut Can
arXiv:2607. 15232v1 Announce Type: cross Abstract: A tokenizer fixed at the start of pre-training allocates vocabulary in proportion to the pre-training corpus, reflecting the deployment priorities at that time.
By Jimmy T. H. Smith, Tarek Dakhran, Alberto Cabrera, Simon S. Lee, Paul Pak, Aditya Tadimeti, Tim Seyde, Maxime Labonne, Alexander Amini, Mathias Lechner
The paper introduces the Latent Core Tokenizer (LCT), a language‑agnostic method that first discovers reusable linguistic units using Minimum Description Length, entropy‑based boundary signals, and morphotactic constraints before building a shared vocabulary. With a 200K‑token vocabulary across 104 languages, LCT shows lower fertility and higher MorphScore than BPE, Unigram, and parity‑aware BPE, while keeping tokenization cost comparable across languages. On four multilingual downstream benchmarks, LCT outperforms the baselines by 1.48, 1.83, and 2.00 aggregate points, demonstrating that compression alone does not guarantee representation quality and underscoring the role of morphology‑driven structural discovery.
By Felermino D. M. A. Ali, Millicent Ochieng, Ogbemi Ekwejunor-Etchie, Ade Famoti, Jacki O'Neill, Debjit Paul