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

2025-02-27

AI Summary

This work addresses the challenge of jointly controlling text editing across multiple scales—from coarse-grained stylistic attributes to fine-grained lexical choices—and diverse semantic attributes (e.g., toxicity, sentiment). We propose the first diffusion-based two-stage controllable editing framework: Stage I employs SDEdit for global, style-level coarse editing; Stage II introduces a self-conditioning mechanism enabling localized, fine-grained semantic refinement. Crucially, our framework is the first to decouple and jointly regulate editing strength and semantic granularity within diffusion language models. Extensive experiments on multiple benchmarks demonstrate substantial improvements in both edit fidelity and attribute control: toxicity reduction increases by 23.6%, and sentiment reversal accuracy improves by 18.4%. The method establishes a novel paradigm for controllable text generation, advancing the state of the art in diffusion-based language modeling.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Natural Language Processing: Sentence-level Semantics, Textual Inference, etc.
• Machine Learning: Large Multimodal Models (LMMs)

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Large language models for search
• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work

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 self-conditioning, 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.

Problem

• Controllable text editing
• Adjust reference text attributes
• Fine-grained and broad text adjustments

Innovation

• Controllable text editing
• SDEdit-based technique
• Fine-level self-conditioning method