Improving Discrete Diffusion Models via Structured Preferential Generation

Autor:	Rissanen, Severi, Heinonen, Markus, Solin, Arno
Rok vydání:	2024
Předmět:	Computer Science - Machine Learning
Druh dokumentu:	Working Paper
Popis:	In the domains of image and audio, diffusion models have shown impressive performance. However, their application to discrete data types, such as language, has often been suboptimal compared to autoregressive generative models. This paper tackles the challenge of improving discrete diffusion models by introducing a structured forward process that leverages the inherent information hierarchy in discrete categories, such as words in text. Our approach biases the generative process to produce certain categories before others, resulting in a notable improvement in log-likelihood scores on the text8 dataset. This work paves the way for more advances in discrete diffusion models with potentially significant enhancements in performance. Comment: 10 pages, 7 figures
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2405.17889 Zobrazit plný text záznamu View this record from Arxiv