Summary of Segment-level Diffusion: a Framework For Controllable Long-form Generation with Diffusion Language Models, by Xiaochen Zhu et al.
Segment-Level Diffusion: A Framework for Controllable Long-Form Generation with Diffusion Language Models
by Xiaochen Zhu, Georgi Karadzhov, Chenxi Whitehouse, Andreas Vlachos
First submitted to arxiv on: 15 Dec 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Diffusion models have shown promise in text generation but struggle with generating long, coherent, and contextually accurate text due to limitations in token-level and passage-level diffusion methods. To address these challenges, we propose Segment-Level Diffusion (SLD), a framework that enhances diffusion-based text generation through text segmentation, robust representation training with adversarial and contrastive learning, and improved latent-space guidance. SLD simplifies diffusion predictions by segmenting long-form outputs into separate latent representations and decoding them with an autoregressive decoder, improving scalability. Experiments on XSum, ROCStories, DialogSum, and DeliData demonstrate that SLD achieves competitive or superior performance in fluency, coherence, and contextual compatibility across automatic and human evaluation metrics compared to other diffusion and autoregressive baselines. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about improving a type of artificial intelligence called text generation. Current methods struggle to create long pieces of text that make sense and are accurate. The new method, Segment-Level Diffusion (SLD), helps by breaking down the task into smaller chunks and using different learning strategies to get better results. Tests on different datasets show that SLD performs well in terms of how fluent, coherent, and accurate the generated text is. |
Keywords
» Artificial intelligence » Autoregressive » Decoder » Diffusion » Latent space » Text generation » Token
