Improving Text Generation with Embedding Diffusion Models: Addressing Challenges in Embedding Space and Denoising

Diffusion models have shown great potential for high-quality data generation, but their exploration in the text domain is still at an early stage. This paper systematically studies the optimization challenges encountered with both the embedding space and the denoising model in embedding diffusion models, and proposes effective solutions to address these challenges.