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    <abstract>Large language models (LLMs) often encode word-form variation (e.g., *walk* vs. *walk**ed***) as linear directions in the embedding space. However, standard tokenization algorithms treat such variants as distinct words with different vocabulary entries—quickly filling the size-capped token vocabulary with surface-form variation (e.g., *walk*, *walk**ing***, ***W**alk*), at the expense of diversity and multilingual coverage. We show that many of these variations can be captured by *transformation* vectors—additive offsets that yield the appropriate word representation when applied to a *base form* embedding, in both the input and output spaces. Building on this, we propose a compact reshaping of the vocabulary: instead of assigning unique tokens to each surface form, we compose them from shared *base form* and *transformation* vectors (e.g., *walked* is *walk*+*past tense*). Our approach is lightweight—keeping the pretrained backbone frozen and only training small adaptation modules. We apply it across five languages and multiple LLMs in both pretraining and post-hoc adaptation, freeing 10-40% of vocabulary slots to be reallocated where tokenization is inefficient. Importantly, we do so while also expanding vocabulary coverage to out-of-vocabulary words, and with minimal impact on downstream performance. Our findings motivate a rethinking of vocabulary design, towards a representation that better matches the underlying structure of language and the practical needs of multilingual coverage.</abstract>
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%0 Conference Proceedings
%T Vocab Diet: Reshaping the Vocabulary of LLMs via Vector Arithmetic
%A Reif, Yuval
%A Kaplan, Guy
%A Schwartz, Roy
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F reif-etal-2026-vocab
%X Large language models (LLMs) often encode word-form variation (e.g., *walk* vs. *walk**ed***) as linear directions in the embedding space. However, standard tokenization algorithms treat such variants as distinct words with different vocabulary entries—quickly filling the size-capped token vocabulary with surface-form variation (e.g., *walk*, *walk**ing***, ***W**alk*), at the expense of diversity and multilingual coverage. We show that many of these variations can be captured by *transformation* vectors—additive offsets that yield the appropriate word representation when applied to a *base form* embedding, in both the input and output spaces. Building on this, we propose a compact reshaping of the vocabulary: instead of assigning unique tokens to each surface form, we compose them from shared *base form* and *transformation* vectors (e.g., *walked* is *walk*+*past tense*). Our approach is lightweight—keeping the pretrained backbone frozen and only training small adaptation modules. We apply it across five languages and multiple LLMs in both pretraining and post-hoc adaptation, freeing 10-40% of vocabulary slots to be reallocated where tokenization is inefficient. Importantly, we do so while also expanding vocabulary coverage to out-of-vocabulary words, and with minimal impact on downstream performance. Our findings motivate a rethinking of vocabulary design, towards a representation that better matches the underlying structure of language and the practical needs of multilingual coverage.
%U https://aclanthology.org/2026.findings-acl.1618/
%P 32334-32352

Markdown (Informal)

[Vocab Diet: Reshaping the Vocabulary of LLMs via Vector Arithmetic](https://aclanthology.org/2026.findings-acl.1618/) (Reif et al., Findings 2026)

• Vocab Diet: Reshaping the Vocabulary of LLMs via Vector Arithmetic (Reif et al., Findings 2026)

ACL

• Yuval Reif, Guy Kaplan, and Roy Schwartz. 2026. Vocab Diet: Reshaping the Vocabulary of LLMs via Vector Arithmetic. In Findings of the Association for Computational Linguistics: ACL 2026, pages 32334–32352, San Diego, California, United States. Association for Computational Linguistics.