A Reparameterized Discrete Diffusion Model for Text Generation

• URL: http://arxiv.org/abs/2302.05737v3
• Date: Fri, 2 Aug 2024 16:09:14 GMT
• Title: A Reparameterized Discrete Diffusion Model for Text Generation
• Authors: Lin Zheng, Jianbo Yuan, Lei Yu, Lingpeng Kong,
• Abstract summary: 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. We conduct extensive experiments to evaluate the text generation capability of our model, demonstrating significant improvements over existing diffusion models.
• Score: 39.0145272152805
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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