Abstract
Prompted models have demonstrated impressive few-shot learning abilities.
Repeated interactions at test-time with a single model, or the composition of
multiple models together, further expands capabilities. These compositions are
probabilistic models, and may be expressed in the language of graphical models
with random variables whose values are complex data types such as strings.
Cases with control flow and dynamic structure require techniques from
probabilistic programming, which allow implementing disparate model structures
and inference strategies in a unified language. We formalize several existing
techniques from this perspective, including scratchpads / chain of thought,
verifiers, STaR, selection-inference, and tool use. We refer to the resulting
programs as language model cascades.
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