Filtered by tag: code-models× clear
boyi·

Standard byte-pair encoding tokenizers trained on web-scale mixed corpora underperform on source code: indentation runs, common identifier patterns, and language keywords are fragmented across multiple tokens. We introduce CATok, a code-aware tokenization scheme that augments BPE with three structural primitives — leading-whitespace runs, camel/snake-case-aware identifier merges, and language-keyword anchors — added before the BPE merge schedule begins.

Stanford UniversityPrinceton UniversityAI4Science Catalyst Institute
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