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

• URL: http://arxiv.org/abs/2105.13626v1
• Date: Fri, 28 May 2021 07:03:22 GMT
• Title: ByT5: Towards a token-free future with pre-trained byte-to-byte models
• Authors: Linting Xue, Aditya Barua, Noah Constant, Rami Al-Rfou, Sharan Narang, Mihir Kale, Adam Roberts, Colin Raffel
• Abstract summary: Most widely-used pre-trained language models operate on sequences of tokens corresponding to word or subword units. We show that a standard Transformer architecture can be used with minimal modifications to process byte sequences. We also demonstrate that byte-level models are significantly more robust to noise and perform better on tasks that are sensitive to spelling and pronunciation.
• Score: 23.532359202069063
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Most widely-used pre-trained language models operate on sequences of tokens corresponding to word or subword units. Encoding text as a sequence of tokens requires a tokenizer, which is typically created as an independent artifact from the model. Token-free models that instead operate directly on raw text (bytes or characters) have many benefits: they can process text in any language out of the box, they are more robust to noise, and they minimize technical debt by removing complex and error-prone text preprocessing pipelines. Since byte or character sequences are longer than token sequences, past work on token-free models has often introduced new model architectures designed to amortize the cost of operating directly on raw text. In this paper, we show that a standard Transformer architecture can be used with minimal modifications to process byte sequences. We carefully characterize the trade-offs in terms of parameter count, training FLOPs, and inference speed, and show that byte-level models are competitive with their token-level counterparts. We also demonstrate that byte-level models are significantly more robust to noise and perform better on tasks that are sensitive to spelling and pronunciation. As part of our contribution, we release a new set of pre-trained byte-level Transformer models based on the T5 architecture, as well as all code and data used in our experiments.

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