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Most widely-used pre-trained language models operate on sequences of tokenscorresponding to word or subword units. By comparison, token-free models thatoperate directly on raw text (bytes or characters) have many benefits: they canprocess text in any language out of the box, they are more robust to noise, andthey minimize technical debt by removing complex and error-prone textpreprocessing pipelines. Since byte or character sequences are longer thantoken sequences, past work on token-free models has often introduced new modelarchitectures designed to amortize the cost of operating directly on raw text.In this paper, we show that a standard Transformer architecture can be usedwith minimal modifications to process byte sequences. We characterize thetrade-offs in terms of parameter count, training FLOPs, and inference speed,and show that byte-level models are competitive with their token-levelcounterparts. We also demonstrate that byte-level models are significantly morerobust to noise and perform better on tasks that are sensitive to spelling andpronunciation. As part of our contribution, we release a new set of pre-trainedbyte-level Transformer models based on the T5 architecture, as well as all codeand data used in our experiments.

ByT5: Towards a token-free future with pre-trained byte-to-byte models