A Reparameterized Discrete Diffusion Model for Text Generation
This work addresses text generation challenges for natural language processing applications, representing an incremental advancement in diffusion model techniques.
The authors tackled the problem of text generation using discrete diffusion models by developing a reparameterized framework that offers more effective training and decoding, resulting in significant improvements over existing diffusion models.
This work studies discrete diffusion probabilistic models with applications to natural language generation. We derive an alternative yet equivalent formulation of the sampling from discrete diffusion processes and leverage this insight to develop a family of reparameterized discrete diffusion models. The derived generic framework is highly flexible, offers a fresh perspective of the generation process in discrete diffusion models, and features more effective training and decoding techniques. We conduct extensive experiments to evaluate the text generation capability of our model, demonstrating significant improvements over existing diffusion models.