Paper · arXiv 2311.08552
UT5: Pretraining Non autoregressive T5 with unrolled denoising
Mahmoud G. Salem, Jiayu Ye, Chu-Cheng Lin, Frederick Liu
Unsupervised pretraining of non-autoregressive T5 models using unrolled denoising achieves state-of-the-art results in downstream generation tasks.
Abstract
Recent advances in Transformer-based Large Language Models have made great strides in natural language generation. However, to decode K tokens, an autoregressive model needs K sequential forward passes, which may be a performance bottleneck for large language models. Many non-autoregressive (NAR) research are aiming to address this sequentiality bottleneck, albeit many have focused on a dedicated architecture in supervised benchmarks. In this work, we studied unsupervised pretraining for non auto-regressive T5 models via unrolled denoising and shown its SoTA results in downstream generation tasks such as SQuAD question generation and XSum.