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Large language models show impressive results at predicting structured textsuch as code, but also commonly introduce errors and hallucinations in theiroutput. When used to assist software developers, these models may make mistakesthat users must go back and fix, or worse, introduce subtle bugs that users maymiss entirely. We propose Randomized Utility-driven Synthesis of UncertainREgions (R-U-SURE), an approach for building uncertainty-aware suggestionsbased on a decision-theoretic model of goal-conditioned utility, using randomsamples from a generative model as a proxy for the unobserved possible intentsof the end user. Our technique combines minimum-Bayes-risk decoding, dualdecomposition, and decision diagrams in order to efficiently produce structureduncertainty summaries, given only sample access to an arbitrary generativemodel of code and an optional AST parser. We demonstrate R-U-SURE on threedeveloper-assistance tasks, and show that it can be applied different userinteraction patterns without retraining the model and leads to more accurateuncertainty estimates than token-probability baselines. We also release ourimplementation as an open-source library athttps://github.com/google-research/r_u_sure.

R-U-SURE? Uncertainty-Aware Code Suggestions By Maximizing Utility Across Random User Intents