Papers with parallel text generation
One Token Is Enough: Improving Diffusion Language Models with a Sink Token (2026.findings-acl)
Copied to clipboard
| Challenge: | Existing Diffusion Language Models lack a structural constraint to stabilize attention sinks. |
| Approach: | They propose a simple but effective extra sink token that is constrained to attend to itself while remaining globally visible to all other tokens. |
| Outcome: | The proposed token is able to stabilize attention sinks and improve model performance. |
latent-GLAT: Glancing at Latent Variables for Parallel Text Generation (2022.acl-long)
Copied to clipboard
| Challenge: | Recent advances in text generation have limited applications due to multimodality problem. |
| Approach: | They propose a method which uses latent variables to capture word categorical information and invoke an advanced curriculum learning technique to overcome multi-modality problem. |
| Outcome: | The proposed method outperforms strong baselines without an autoregressive model, which further broadens the application scenarios of the parallel decoding paradigm. |
From AR to Diffusion: Efficiently Adapting Large Language Models with Strictly Causal and Elastic Horizons (2026.acl-long)
Copied to clipboard
| Challenge: | Autoregressive (AR) models rely on bidirectional attention, creating a structural mismatch with pre-trained Autoregression models. |
| Approach: | They propose a framework that efficiently adapts autoregressive (AR) models to the diffusion paradigm. |
| Outcome: | The proposed framework reduces training costs by orders of magnitude while maintaining state-of-the-art performance. |