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Status: Unchecked ×Keyword: unconditional generation ×Clear all

2 claims from 1 paper

1. Computer Science › Topic Modeling

   GLM: General Language Model Pretraining with Autoregressive Blank Infilling

   Du, Qian, Liu et al. · arXiv (Cornell University) · 2021

   The authors propose GLM, a pretraining method based on autoregressive blank infilling, and report that it performs well across understanding, conditional generation and unconditional generation tasks.

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   1. UncheckedGLM is reported to outperform BERT, T5 and GPT on language understanding and generation tasks at matched size and data, using one pretrained model.“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 1.25x parameters of BERT Large , demonstrating its generalizability to differen…”
   2. UncheckedThe GLM model can be pretrained for different kinds of tasks by changing how many blanks it fills in and how long each blank is.“Meanwhile, GLM can be pretrained for different types of tasks by varying the number and lengths of blanks.”