TokDrift: When LLM Speaks in Subwords but Code Speaks in Grammar (2026.acl-long)
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| Challenge: | Large language models (LLMs) for code rely on subword tokenizers learned from mixed natural language text and programming language code but driven by statistics rather than grammar. |
| Approach: | They propose a framework that applies semantic-preserving rewrite rules to create code variants differing only in tokenization. |
| Outcome: | The proposed framework can create code variants differing only in tokenization . the findings highlight the need for grammar-aware tokenization for future code LLMs. |
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| Challenge: | Language models typically tokenize raw text into sequences of subword identifiers from a predefined vocabulary. |
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Unlike “Likely”, “Unlike” is Unlikely: BPE-based Segmentation hurts Morphological Derivations in LLMs (2025.coling-main)
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| Challenge: | Large Language Models (LLMs) use subword vocabularies to process and generate text. |
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How Tokenization Limits Phonological Knowledge Representation in Language Models and How to Improve Them (2026.acl-long)
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| Challenge: | Tokenization is the first step in every language model (LM), yet it never takes the sounds of words into account. |
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Grammar-Based Code Representation: Is It a Worthy Pursuit for LLMs? (2025.findings-acl)
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Qingyuan Liang, Zhao Zhang, Zeyu Sun, Zheng Lin, Qi Luo, Xiao Yueyi, Yizhou Chen, Yuqun Zhang, Haotian Zhang, Lu Zhang, Chenbin Chenbin, Yingfei Xiong
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Tokenization is Sensitive to Language Variation (2025.findings-acl)
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| Challenge: | Variation in language is often linked to regional, social, and contextual factors. |
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TokAlign: Efficient Vocabulary Adaptation via Token Alignment (2025.acl-long)
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| Challenge: | Tokenization is a foundational step for Large Language Models (LLMs) but low compression rate of vanilla tokenizers decelerates training and inference process. |
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Vocab Diet: Reshaping the Vocabulary of LLMs via Vector Arithmetic (2026.findings-acl)
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| Challenge: | Large language models often encode word-form variation as linear directions in the embedding space. |
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When Format Changes Meaning: Investigating Semantic Inconsistency of Large Language Models (2025.findings-emnlp)
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| Challenge: | Large language models are vulnerable to semantic inconsistency, a study finds . minor formatting variations result in divergent predictions for semantically equivalent inputs. |
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Learn Your Tokens: Word-Pooled Tokenization for Language Modeling (2023.findings-emnlp)
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| Challenge: | Language models typically tokenize text into subwords, using a deterministic, hand-engineered heuristic of combining characters into longer surface-level strings such as ‘ing’ or whole words. |
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Token Erasure as a Footprint of Implicit Vocabulary Items in LLMs (2024.emnlp-main)
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| Challenge: | Current language models process text as sequences of tokens that roughly correspond to words . individual tokens are often semantically unrelated to the meanings of the words/concepts they comprise . |
| Approach: | They propose a method to "read out" the implicit vocabulary of an autoregressive LLM by examining differences in token representations across layers. |
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