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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
The Devil behind the mask: An emergent safety vulnerability of Diffusion LLMsCode2
Flexible-length Text Infilling for Discrete Diffusion Models—0
LaViDa: A Large Diffusion Language Model for Multimodal UnderstandingCode3
Insertion Language Models: Sequence Generation with Arbitrary-Position Insertions—0
Enhancing Spoken Discourse Modeling in Language Models Using Gestural Cues—0
TrajGPT: Controlled Synthetic Trajectory Generation Using a Multitask Transformer-Based Spatiotemporal ModelCode0
Empowering Character-level Text Infilling by Eliminating Sub-TokensCode0
Towards Probabilistically-Sound Beam Search with Masked Language ModelsCode0
A Benchmark for Text Expansion: Datasets, Metrics, and Baselines—0
A Simple yet Effective Framework for Few-Shot Aspect-Based Sentiment AnalysisCode1
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