From Where Words Come: Efficient Regularization of Code Tokenizers Through Source Attribution
Efficiency and safety of Large Language Models (LLMs), among other factors, rely on the quality of tokenization. A good tokenizer not only improves inference speed and language understanding but also provides extra defense against jailbreak attacks and lowers the risk of hallucinations. In this work, we investigate the efficiency of code tokenization, in particular from the perspective of data source diversity. We demonstrate that code tokenizers are prone to producing unused, and thus under-trained, tokens due to the imbalance in repository and language diversity in the training data, as well as the dominance of source-specific, repetitive tokens that are often unusable in future inferen...
| Authors | Pavel Chizhov, Egor Bogomolov, Ivan P. Yamshchikov |
|---|---|
| Primary category | cs.CL |
| Published | 15 April 2026 |
| Last updated at source | 15 April 2026 |
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