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Constrained Decoding Models: three task-specific models for classify, extract, summarize

1 article1 sourcesince Oct 13Last article 1h ago ·

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Neal K. Patel introduces Constrained Decoding Models (CDMs), a family of three task-specific models for classification, extraction and summarization.

Patel's launch post lists $0.05 per million input tokens, free output including for summarization, and 50 million free input tokens for the first 50 hours through a link he gives.

Santiago (@svpino) says the white paper's classification and extraction skip token generation. A task-specific head reads the input's internal representation and selects a label from a supplied set, or a span of source text with offsets and confidence scores. Santiago says the model can still pick a wrong label or passage, but the restricted answer space makes outputs easier to validate.

Written by AI from the articles below · updated Oct 13, 9:45 AM ET

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Latest developmentOct 13, 9:32 AM ET
Constrained Decoding Models skip token generation for classification and extraction

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Oct 13
  1. SantiagoX
    Constrained Decoding Models skip token generation for classification and extraction

    AISantiago (@svpino) says the white paper's classification and span extraction use a task-specific head that reads the input's internal representation and selects a label or source span. Classification returns labels from a supplied set, and extraction returns input text with source offsets and confidence scores. The model can still pick a wrong label or passage, but restricting the answer space makes its outputs easier to validate.

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