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- sl:arxiv_author :
- sl:arxiv_firstAuthor : Yichi Zhang
- sl:arxiv_num : 2302.04907
- sl:arxiv_published : 2023-02-09T19:27:34Z
- sl:arxiv_summary : The rapid scaling of language models is motivating research using
low-bitwidth quantization. In this work, we propose a novel binarization
technique for Transformers applied to machine translation (BMT), the first of
its kind. We identify and address the problem of inflated dot-product variance
when using one-bit weights and activations. Specifically, BMT leverages
additional LayerNorms and residual connections to improve binarization quality.
Experiments on the WMT dataset show that a one-bit weight-only Transformer can
achieve the same quality as a float one, while being 16x smaller in size.
One-bit activations incur varying degrees of quality drop, but mitigated by the
proposed architectural changes. We further conduct a scaling law study using
production-scale translation datasets, which shows that one-bit weight
Transformers scale and generalize well in both in-domain and out-of-domain
settings. Implementation in JAX/Flax will be open sourced.@en
- sl:arxiv_title : Binarized Neural Machine Translation@en
- sl:arxiv_updated : 2023-02-09T19:27:34Z
- sl:bookmarkOf : https://arxiv.org/abs/2302.04907
- sl:creationDate : 2023-02-13
- sl:creationTime : 2023-02-13T14:51:45Z
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