The paper proposes a self‑supervised framework that trains language models to predict concepts—sets of semantically equivalent tokens—rather than single tokens. This approach improves alignment with human similarity judgments, boosts performance on classification, clustering, and reranking tasks, and yields comparable or stronger downstream reasoning while lowering perplexity on semantically meaningful words and only slightly increasing overall perplexity.
By Christine Zhang, Dan Jurafsky, Chen Shani
arXiv:2605.25263v2 Announce Type: replace-cross
Abstract: Current language modeling approaches are built around tokens. Text corpora are split into tokens, and models are trained by performing comput...
By Elio Musacchio, Lucia Siciliani, Pierpaolo Basile
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
The paper proposes a modular tokenizer framework for multilingual large language models, allowing the creation of language‑specific subtokenizers that match monolingual compression quality. It introduces a pretraining strategy that samples these subtokenizers to limit predictions to relevant vocabularies, enabling efficient training and inference. This approach reduces memory usage and speeds up inference without compromising performance.
By Franck Signe, Hippolyte Pilchen, Fran\c{c}ois Yvon, \'Edouard Grave
The paper examines how new vocabulary tokens are added to language models for generative recommendation tasks. It shows that the common practice of initializing these tokens as the mean of existing embeddings collapses them into a degenerate subspace, hindering fine‑tuning. The authors propose Grounded Token Initialization (GTI), which places new tokens at semantically meaningful positions in the pretrained embedding space using linguistic supervision, and demonstrate that GTI outperforms mean initialization and other adaptation methods across several benchmarks.
By Daiwei Chen, Zhoutong Fu, Chengming Jiang, Haichao Zhang, Ran Zhou, Tan Wang, Chunnan Yao, Guoyao Li, Rui Cai, Yihan Cao, Ruijie Jiang, Fedor Borisyuk, Jianqiang Shen, Jingwei Wu, Ramya Korlakai Vinayak
The paper investigates whether intrinsic tokenizer features can predict the performance of downstream language models. By training about 200 models across nine languages and evaluating numerous tokenizer metrics, the authors find that none of the existing metrics consistently forecasts model performance across different families, tasks, and languages. They propose a new linguistically grounded metric that captures aspects of tokenizer quality missed by prior measures, though its predictive power may still vary.
By Antonio Lopardo, Luis Frentzen Salim, Shikhar Srivastava, Kevin Jiang, Mridul Sharma, Catherine Arnett