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Diff4TST: Masked Diffusion Language Model for Text Style Transfer
来源日期（仅到月份） 2026-07
来源记录日期 2026-07
雷达观测 2026-11-01 22:04
摘要
Despite recent progress in LLMs for text style transfer, most existing methods rely on costly task-specific training and offer limited control over separating stylistic modification from content preservation. We propose Diff4TST, a diffusion-based language model that formulates text style transfer as an explicit copy-and-edit process. Built upon masked diffusion language models, Diff4TST introduces a style-aware noise schedule that selectively perturbs stylistic tokens while preserving content-bearing tokens during supervised fine-tuning.At inference time, we further introduce a generate-then-refine strategy that iteratively improves style compliance via gradient-based token re-masking, without reinforcement learning or external reward models. Extensive experiments on both fine-grained and polarity-based benchmarks show that Diff4TST achieves substantially improved style accuracy and controllability while maintaining strong content preservation and fluency. These results suggest diffusion-based language models as a principled and effective alternative to autoregressive pipelines for text style transfer.
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记录 #1 · 2026-07 · 最新观测版本
Diff4TST: Masked Diffusion Language Model for Text Style Transfer
Despite recent progress in LLMs for text style transfer, most existing methods rely on costly task-specific training and offer limited control over separating stylistic modification from content preservation. We propose Diff4TST, a diffusion-based language model that formulates text style transfer as an explicit copy-and-edit process. Built upon masked diffusion language models, Diff4TST introduces a style-aware noise schedule that selectively perturbs stylistic tokens while preserving content-bearing tokens during supervised fine-tuning.At inference time, we further introduce a generate-then-refine strategy that iteratively improves style compliance via gradient-based token re-masking, without reinforcement learning or external reward models. Extensive experiments on both fine-grained and polarity-based benchmarks show that Diff4TST achieves substantially improved style accuracy and controllability while maintaining strong content preservation and fluency. These results suggest diffusion-based language models as a principled and effective alternative to autoregressive pipelines for text style transfer.
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