AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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It is argued that framing tokenization as output supervision provides a principled account of why tokenization consistently affects model performance, and that differences in task performance, training dynamics, and model internals are induced by output tokenization and largely invariant to input tokenization.
It is shown that translation is even more modular than previously assumed and that the output language production in translation processes is actually further separable into a syntax and a surface language process.
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