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Researchers decompose what makes tokenizers effective in language models

Paper decomposes tokenization algorithm design choices

1 Sep 17 12:00 AM · 9d ago · 1 article · 1 source · development 1 of 2

Researchers published "Objective vs. Search: Decomposing What Makes a Good Tokeniser" on arXiv, separating the two axes along which BPE and UnigramLM differ—optimization objective and search procedure—to clarify what drives their observed performance differences.

Paper decomposes tokenization algorithm design choices
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Ahmetcan Yavuz Lead authorClara Meister Co-authorTiago Pimentel Co-author and lead communicator

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1Paper decomposes tokenization algorithm…2Lead author highlights finding on…
Sep 17Sep 18Sep 19Sep 20Sep 21Sep 22Sep 23Sep 24yesterdaynow · 5:15 PM ET

What was reported 1 claim about this development

  1. 1 outlet Objective vs. Search: Decomposing What Makes a Good Tokeniser

    first by arXiv cs.AI, 9d ago

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