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EdiText: Controllable Coarse-to-Fine Text Editing with Diffusion Language Models

Che Hyun Lee, Heeseung Kim, Jiheum Yeom, Sungroh Yoon

2025Year

Abstract

We propose EdiText, a controllable text editing method that modify the reference text to desired attributes at various scales. We integrate an SDEdit-based editing technique that allows for broad adjustments in the degree of text editing. Additionally, we introduce a novel fine-level editing method based on selfconditioning, which allows subtle control of reference text. While being capable of editing on its own, this fine-grained method, integrated with the SDEdit approach, enables EdiText to make precise adjustments within the desired range. EdiText demonstrates its controllability to robustly adjust reference text at broad range of levels across various tasks, including toxicity control and sentiment control.

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