ShortListing Model: A Streamlined SimplexDiffusion for Discrete Variable Generation
Abstract
The Shortlisting Model (SLM), a simplex-based diffusion model with classifier-free guidance, demonstrates competitive performance in discrete variable generation tasks such as DNA and protein design, and language modeling.
Generative modeling of discrete variables is challenging yet crucial for applications in natural language processing and biological sequence design. We introduce the Shortlisting Model (SLM), a novel simplex-based diffusion model inspired by progressive candidate pruning. SLM operates on simplex centroids, reducing generation complexity and enhancing scalability. Additionally, SLM incorporates a flexible implementation of classifier-free guidance, enhancing unconditional generation performance. Extensive experiments on DNA promoter and enhancer design, protein design, character-level and large-vocabulary language modeling demonstrate the competitive performance and strong potential of SLM. Our code can be found at https://github.com/GenSI-THUAIR/SLM
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