Large Language Diffusion Models (LLaDA) represent a paradigm shift in the field of text generation, diverging from traditional autoregressive models (ARMs) by employing a diffusion-like approach. Instead of generating tokens in a sequential, left-to-right manner, LLaDA allows for progressively refining masked text into coherent responses. Understanding LLaDA necessitates a grasp of the existing two-step training methods of traditional LLMs: pre-training and supervised fine-tuning, which focus heavily on next-token predictions. This article explores the mechanics of LLaDA, its significance in natural language processing, and potential implications for future LLM development.
Large Language Diffusion Models (LLaDA) represent a significant shift in text generation by refining masked text progressively rather than predicting one token at a time.
LLaDA introduces a diffusion-like process for language generation, allowing for a more human-like thought process by sketching out ideas and refining them.
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