Researchers at Stanford Introduce Score Entropy Discrete Diffusion (SEDD): A Machine Learning Model that Challenges the Autoregressive Language Paradigm and Beats GPT-2 on Perplexity and Quality

Researchers at Stanford have introduced a machine learning model called Score Entropy Discrete Diffusion (SEDD) that challenges the autoregressive language paradigm. SEDD surpasses GPT-2 in terms of perplexity and quality. It addresses the limitations of both autoregressive and diffusion models in text generation and provides better control over the text production process.