Papers with subword-based tokenization
WECHSEL: Effective initialization of subword embeddings for cross-lingual transfer of monolingual language models (2022.naacl-main)
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| Challenge: | Existing methods to train large pretrained language models require more computational resources and are expensive to train in other languages. |
| Approach: | They propose a method to transfer pretrained language models to new languages using subword-based tokenization and embeddings. |
| Outcome: | The proposed method outperforms existing methods on low-resource languages and makes training large models more accessible and less damaging to the environment. |
Small Language Models Also Work With Small Vocabularies: Probing the Linguistic Abilities of Grapheme- and Phoneme-Based Baby Llamas (2025.coling-main)
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| Challenge: | Existing studies on LMs have focused on linguistic generalizations and representations from developmentally plausible data. |
| Approach: | They propose to use phoneme- and grapheme-based language models to learn linguistic units at and below the word level. |
| Outcome: | The proposed models can achieve strong performance on syntactic and novel benchmarks and match grapheme-based models in standard tasks and novel evaluations. |
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. |
| Approach: | They propose a lightweight IPA-based fine-tuning method that infuses phonological awareness into LMs. |
| Outcome: | The proposed method improves phonological awareness across three phonology-related tasks while preserving math and general reasoning ability. |