This paper introduces a novel approach for improving the quality and consistency of outputs from large-scale pre-trained language models. The approach extends self-consistency to problems without fixed-answer answers and shows consistent improvements across various tasks without additional computational overhead.

https://arxiv.org/abs//2307.06857

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