Enforcing canonical BPE tokenizations through conditioning or architectural constraints improves held-out likelihood for GPT-2 and Llama models.
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Language Models over Canonical Byte-Pair Encodings
Enforcing canonical BPE tokenizations through conditioning or architectural constraints improves held-out likelihood for GPT-2 and Llama models.