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About this paper
Kehang Han, Kathleen Kenealy, Aditya Barua, Noah Fiedel, Noah Constant
Researchers from Google DeepMind and Google investigate text diffusion models within the transfer learning paradigm, introducing the AR2Diff adaptation method to leverage existing autoregressive models. Their findings show diffusion models achieve competitive performance on tasks like question answering and outperform for code synthesis, while offering significantly faster inference for long text generation.
Key insights
- Decoder-only architectures consistently outperform encoder-decoder models for text diffusion, particularly in code synthesis.
- The prefix language modeling objective is more effective for pretraining text diffusion models than span corruption.
- The AR2Diff method successfully transfers knowledge from pretrained autoregressive models to diffusion models, with longer adaptation periods generally yielding better results.
Summary by alphaXiv.
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