Papers with self-conditioning
TESS: Text-to-Text Self-Conditioned Simplex Diffusion (2024.eacl-long)
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Rabeeh Karimi Mahabadi, Hamish Ivison, Jaesung Tae, James Henderson, Iz Beltagy, Matthew Peters, Arman Cohan
| Challenge: | Existing models for diffusion generation are expensive and discrete, resulting in a large number of diffusion steps to generate text. |
| Approach: | They propose a text diffusion model that is fully non-autoregressive and employs a new form of self-conditioning and applies the diffusion process on the logit simplex space rather than the learned embedding space. |
| Outcome: | The proposed model outperforms state-of-the-art non-autoregressive models, requires fewer diffusion steps with minimal drop in performance, and is competitive with pretrained autoregressive sequence-to-sequence models. |
EdiText: Controllable Coarse-to-Fine Text Editing with Diffusion Language Models (2025.acl-long)
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| Challenge: | Existing methods for text editing have been proposed for various types of data with diverse attributes. |
| Approach: | They propose a novel text editing method that modifies reference text to desired attributes at various scales. |
| Outcome: | The proposed method is capable of making precise adjustments within the desired range while maintaining the accuracy of the reference text. |