arXiv:2605.10938v2 Announce Type: replace-cross Abstract: Diffusion and flow-based models have become the de facto approaches for generating continuous data, e.g., in domains such as images and videos. Their success has attracted growing interest in applying them to language modeling. Unlike their image-domain counterparts, today's leading diffusion language models (DLMs) primarily operate over discrete tokens. In this paper, we show that continuous DLMs can be made effective with minimal adaptation to the discrete domain. We propose Embedded Language Flows (ELF), a class of diffusion models i
The paper builds on the growing interest in applying diffusion models, successful in continuous data, to language modeling by showing their effectiveness in a continuous domain.
This research suggests a potential paradigm shift in how language models generate text, moving from discrete token-based systems to continuous representations, which could enhance fluency and creativity.
The primary approach to Diffusion Language Models (DLMs) shifts from predominantly discrete token operations to effective continuous representations, impacting model efficiency and output quality.
- · AI researchers
- · NLP developers
- · Generative AI platforms
- · Discrete token-based language model specialists
Improved fluency and naturalness in AI-generated text.
Reduced computational complexity for certain language generation tasks and potentially more efficient training.
New forms of multimodal AI where language and continuous data (like images/video) are more seamlessly integrated.
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Read at arXiv cs.LG