A Closer Look at Autoregressive and Diffusion-Based Language Models

Autoregressive language models generate text sequentially token-by-token from left to right, excelling in fluency and efficiency but suffering from error propagation and limited parallelism. Diffusion-based language models adapt image generation techniques to text by gradually denoising corrupted sequences, enabling parallel updates and global coherence but requiring multiple inference steps. The comparison reveals trade-offs in speed, quality, and diversity, with autoregressive models being faster for short outputs and diffusion models offering better global coherence. Hybrid approaches like AR-Diffusion combine both paradigms to leverage their complementary strengths.