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Diffusion models have garnered considerable interest in the field of textgeneration. Several studies have explored text diffusion models with differentstructures and applied them to various tasks, including named entityrecognition and summarization. However, there exists a notable disparitybetween the "easy-first" text generation process of current diffusion modelsand the "keyword-first" natural text generation process of humans, which hasreceived limited attention. To bridge this gap, we propose InfoDiffusion, anon-autoregressive text diffusion model. Our approach introduces a"keyinfo-first" generation strategy and incorporates a noise schedule based onthe amount of text information. In addition, InfoDiffusion combinesself-conditioning with a newly proposed partially noising model structure.Experimental results show that InfoDiffusion outperforms the baseline model interms of generation quality and diversity, as well as exhibiting highersampling efficiency.

InfoDiffusion: Information Entropy Aware Diffusion Process for Non-Autoregressive Text Generation