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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 1–10 of 43 papers

TitleStatusHype
LaViDa: A Large Diffusion Language Model for Multimodal UnderstandingCode3
The Devil behind the mask: An emergent safety vulnerability of Diffusion LLMsCode2
A Simple yet Effective Framework for Few-Shot Aspect-Based Sentiment AnalysisCode1
Having Beer after Prayer? Measuring Cultural Bias in Large Language ModelsCode1
MAGVLT: Masked Generative Vision-and-Language TransformerCode1
Generative Prompt Tuning for Relation ClassificationCode1
Reprogramming Pretrained Language Models for Antibody Sequence InfillingCode1
Prompting ELECTRA: Few-Shot Learning with Discriminative Pre-Trained ModelsCode1
CTRLEval: An Unsupervised Reference-Free Metric for Evaluating Controlled Text GenerationCode1
Language modeling via stochastic processesCode1
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