Toward Trustworthy Neural Program Synthesis

Darren Key; Wen-Ding Li; Kevin Ellis

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arXiv · 2210.00848

Toward Trustworthy Neural Program Synthesis

Darren Key·Wen-Ding Li·Kevin Ellis

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Abstract

We develop an approach to estimate the probability that a program sampled from a large language model is correct. Given a natural language description of a programming problem, our method samples both candidate programs as well as candidate predicates specifying how the program should behave. This allows learning a model that forms a well-calibrated probabilistic prediction of program correctness. Our system also infers which predicates are useful to explain the behavior of the generated code, and humans preferred these in a human study over raw language model outputs. Our method is simple, easy to implement, and maintains state of the art generation accuracy results.

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Darren Key, Wen-Ding Li, Kevin Ellis. 2022-09-29. Toward Trustworthy Neural Program Synthesis. https://arxiv.org/abs/2210.00848

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