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    <abstract>There have been various types of pretraining architectures including autoencoding models (e.g., BERT), autoregressive models (e.g., GPT), and encoder-decoder models (e.g., T5). However, none of the pretraining frameworks performs the best for all tasks of three main categories including natural language understanding (NLU), unconditional generation, and conditional generation. We propose a General Language Model (GLM) based on autoregressive blank infilling to address this challenge. GLM improves blank filling pretraining by adding 2D positional encodings and allowing an arbitrary order to predict spans, which results in performance gains over BERT and T5 on NLU tasks. Meanwhile, GLM can be pretrained for different types of tasks by varying the number and lengths of blanks. On a wide range of tasks across NLU, conditional and unconditional generation, GLM outperforms BERT, T5, and GPT given the same model sizes and data, and achieves the best performance from a single pretrained model with 1.25\times parameters of BERT Large , demonstrating its generalizability to different downstream tasks.</abstract>
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%0 Conference Proceedings
%T GLM: General Language Model Pretraining with Autoregressive Blank Infilling
%A Du, Zhengxiao
%A Qian, Yujie
%A Liu, Xiao
%A Ding, Ming
%A Qiu, Jiezhong
%A Yang, Zhilin
%A Tang, Jie
%Y Muresan, Smaranda
%Y Nakov, Preslav
%Y Villavicencio, Aline
%S Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2022
%8 May
%I Association for Computational Linguistics
%C Dublin, Ireland
%F du-etal-2022-glm
%X There have been various types of pretraining architectures including autoencoding models (e.g., BERT), autoregressive models (e.g., GPT), and encoder-decoder models (e.g., T5). However, none of the pretraining frameworks performs the best for all tasks of three main categories including natural language understanding (NLU), unconditional generation, and conditional generation. We propose a General Language Model (GLM) based on autoregressive blank infilling to address this challenge. GLM improves blank filling pretraining by adding 2D positional encodings and allowing an arbitrary order to predict spans, which results in performance gains over BERT and T5 on NLU tasks. Meanwhile, GLM can be pretrained for different types of tasks by varying the number and lengths of blanks. On a wide range of tasks across NLU, conditional and unconditional generation, GLM outperforms BERT, T5, and GPT given the same model sizes and data, and achieves the best performance from a single pretrained model with 1.25\times parameters of BERT Large , demonstrating its generalizability to different downstream tasks.
%R 10.18653/v1/2022.acl-long.26
%U https://aclanthology.org/2022.acl-long.26
%U https://doi.org/10.18653/v1/2022.acl-long.26
%P 320-335

Markdown (Informal)

[GLM: General Language Model Pretraining with Autoregressive Blank Infilling](https://aclanthology.org/2022.acl-long.26) (Du et al., ACL 2022)

• GLM: General Language Model Pretraining with Autoregressive Blank Infilling (Du et al., ACL 2022)

ACL

• Zhengxiao Du, Yujie Qian, Xiao Liu, Ming Ding, Jiezhong Qiu, Zhilin Yang, and Jie Tang. 2022. GLM: General Language Model Pretraining with Autoregressive Blank Infilling. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 320–335, Dublin, Ireland. Association for Computational Linguistics.