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P23: Language Model Cascades
David Dohan · Winnie Xu

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in Beyond Bayes: Paths Towards Universal Reasoning Systems »
Event URL: https://drive.google.com/file/d/1pOFwRDEBvo9QtJgrJy-l7Lna7bfBPgU6/view »

Authors: David Dohan, Aitor Lewkowycz, Jacob Austin, Winnie Xu, Yuhuai Wu, David Bieber, Raphael Gontijo-Lopes, Henryk Michalewski, Rif A. Saurous, Jascha Sohl-Dickstein, Kevin Patrick Murphy, Charles Sutton

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, and allow implementing disparate model structures and inference strategies in a unified language. We describe several existing techniques from this perspective, including scratchpads and chain of thought, verifiers, STaR, selection-inference, and tool use. We refer to the resulting programs as \emph{language model \cascades}.

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Author Information

David Dohan (Google)
Winnie Xu (University of Toronto)
Winnie Xu

Winnie recently graduated with an H.BSc from the University of Toronto where she majored in Computer Science and specialized in Artificial Intelligence. Her research interests span broadly in generative models with probabilistic interpretations and differentiable numerical algorithms. As an undergraduate, she researched latent variable models, variational inference, and Neural ODEs / SDEs with David Duvenaud. She is currently a student researcher at Google Brain collaborating with Stanford University where she is working on efficient methods for training diffusion models and doing Bayesian program induction with large language models in reasoning tasks. In the recent past, she has also collaborated with Nvidia Research, Oxford (OATML), and Cohere AI on topics in robotics, large language models, and NLP.

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