The authors first trained a multiscale residual VQ-VAE, a vector-quantized autoencoder that compresses text into nested sequences of discrete codes.
Coarse-to-fine text generation matches diffusion quality at much higher speed
Next-Concept Modeling generates hierarchical text codes in parallel, reducing sequential model evaluations and computational cost without distillation.
AI Startup
Hermann Kumbong · Ishan S. Khare · Iddah Mlauzi · Andrew Woen · Charles Ding · E. Kelly Buchanan · +3 more
Stanford University · Together AI
Research Digest··2 min read
Kumbong and colleagues introduce Next-Concept Modeling, a two-stage system that represents text at several levels of detail and generates each level in parallel.
Why this paper
From Together AI and Stanford University
In one line
Next-Concept Modeling generates text by predicting all codes within each hierarchical level in parallel, achieving faster generation with fewer steps.
What we could check
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