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HazyResearch introduces Next-Concept Modeling for parallel, coarse-to-fine text generation
HazyResearch claims up to 31× faster generation on summarization and translation than similar-sized diffusion models, with comparable or better quality.
TLDR
HazyResearch's Next-Concept Modeling generates text coarse-to-fine, predicting positions within each level in parallel, according to its GitHub repository. The team claims up to 31× faster generation on summarization and translation than diffusion models of similar parameter size, with comparable or better quality. It also reports 10–21× faster unconditional generation across TinyStories, LM1B and OpenWebText, with comparable or better generative perplexity and 3–5× fewer training tokens. The repository links to model weights.
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