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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

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Showing 21–30 of 43 papers

TitleStatusHype
Decoding As Dynamic Programming For Recurrent Autoregressive Models—0
Don't Prompt, Search! Mining-based Zero-Shot Learning with Language Models—0
Enhancing Spoken Discourse Modeling in Language Models Using Gestural Cues—0
Flexible-length Text Infilling for Discrete Diffusion Models—0
Generative Prompt Tuning for Relation Classification—0
InFillmore: Frame-Guided Language Generation with Bidirectional Context—0
Insertion Language Models: Sequence Generation with Arbitrary-Position Insertions—0
"Mask and Infill" : Applying Masked Language Model to Sentiment Transfer—0
Nutri-bullets Hybrid: Consensual Multi-document Summarization—0
Predicting scalar diversity with context-driven uncertainty over alternatives—0
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