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On Thursday, Inception Labs released Mercury Coder, a new AI language model that uses diffusion techniques to generate text faster than conventional models. Unlike traditional models that create text word by word—such as the kind that powers ChatGPT—diffusion-based models like Mercury produce entire responses simultaneously, refining them from an initially masked state into coherent text.
Traditional large language models build text from left to right, one token at a time. They use a technique called "autoregression." Each word must wait for all previous words before appearing. Inspired by techniques from image-generation models like Stable Diffusion, DALL-E, and Midjourney, text diffusion language models like LLaDA (developed by researchers from Renmin University and Ant Group) and Mercury use a masking-based approach. These models begin with fully obscured content and gradually "denoise" the output, revealing all parts of the response at once.
While image diffusion models add continuous noise to pixel values, text diffusion models can't apply continuous noise to discrete tokens (chunks of text data). Instead, they replace tokens with special mask tokens as the text equivalent of noise. In LLaDA, the masking probability controls the noise level, with high masking representing high noise and low masking representing low noise. The diffusion process moves from high noise to low noise. Though LLaDA describes this using masking terminology and Mercury uses noise terminology, both apply a similar concept to text generation rooted in diffusion.
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Traditional large language models build text from left to right, one token at a time. They use a technique called "autoregression." Each word must wait for all previous words before appearing. Inspired by techniques from image-generation models like Stable Diffusion, DALL-E, and Midjourney, text diffusion language models like LLaDA (developed by researchers from Renmin University and Ant Group) and Mercury use a masking-based approach. These models begin with fully obscured content and gradually "denoise" the output, revealing all parts of the response at once.
While image diffusion models add continuous noise to pixel values, text diffusion models can't apply continuous noise to discrete tokens (chunks of text data). Instead, they replace tokens with special mask tokens as the text equivalent of noise. In LLaDA, the masking probability controls the noise level, with high masking representing high noise and low masking representing low noise. The diffusion process moves from high noise to low noise. Though LLaDA describes this using masking terminology and Mercury uses noise terminology, both apply a similar concept to text generation rooted in diffusion.
Read full article
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