Language Model Cascades

Prompted models have demonstrated impressive In this position paper, we argue that a useful unifying few-shot learning abilities. Repeated interactions framework for understanding and extending this disparate at test-time with a single model, or the body of work is in terms of probabilistic programming languages composition of multiple models together, further (PPL) extended to work with strings, instead of expands capabilities. These compositions are more atomic data types like integers and floats. That is, probabilistic models, and may be expressed in we use a PPL to define a joint probability model on stringvalued the language of graphical models with random random variables, parameterized using LMs, and variables whose values are complex data types then condition this model on string-valued observations in such as strings. Cases with control flow and dynamic order to compute a posterior over string-valued unknowns, structure require techniques from probabilistic which we can then infer. We call such a probabilistic programming, which allow implementing program a language model cascade. We show that this disparate model structures and inference strategies framework captures many recent approaches, and also allows in a unified language. We formalize several us to tackle more complex multi-step reasoning problems.