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Text Infilling

Text Infilling is the task of predicting missing spans of text which are consistent with the preceding and subsequent text. Text Infilling is a generalization of the cloze task—cloze historically refers to infilling individual words.

Source: Enabling Language Models to Fill in the Blanks

Papers

Showing 1120 of 43 papers

TitleStatusHype
MAGVLT: Masked Generative Vision-and-Language TransformerCode1
Improving Sequence-to-Sequence Pre-training via Sequence Span RewritingCode1
Reprogramming Pretrained Language Models for Antibody Sequence InfillingCode1
Generative Prompt Tuning for Relation ClassificationCode1
Model-tuning Via Prompts Makes NLP Models Adversarially RobustCode0
Nutribullets Hybrid: Multi-document Health SummarizationCode0
Empowering Character-level Text Infilling by Eliminating Sub-TokensCode0
MetaFill: Text Infilling for Meta-Path Generation on Heterogeneous Information NetworksCode0
Towards Probabilistically-Sound Beam Search with Masked Language ModelsCode0
TIGS: An Inference Algorithm for Text Infilling with Gradient SearchCode0
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