Papers by Do Huu Dat
Discrete Diffusion Language Model for Efficient Text Summarization (2025.findings-naacl)
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| Challenge: | Existing discrete diffusion models fail on conditional long-text generation due to incompatibility between the backbone architectures and the random noising process. |
| Approach: | They propose a semantic-aware noising process that enables Transformer backbones to handle long sequences effectively. |
| Outcome: | The proposed model outperforms existing models on three benchmark summarization datasets while achieving much faster inference speed compared to autoregressive models. |