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A popular approach for improving the correctness of output from largelanguage models (LLMs) is Self-Consistency - poll the LLM multiple times andoutput the most frequent solution. Existing Self-Consistency techniques alwaysgenerate a constant number of samples per question, where a better approachwill be to non-uniformly distribute the available budget based on the amount ofagreement in the samples generated so far. In response, we introduceAdaptive-Consistency, a cost-efficient, model-agnostic technique thatdynamically adjusts the number of samples per question using a lightweightstopping criterion. Our experiments over 17 reasoning and code generationdatasets and three LLMs demonstrate that Adaptive-Consistency reduces samplebudget by up to 7.9 times with an average accuracy drop of less than 0.1%. Ourcode and data are available at https://www.sample-step-by-step.info

Let's Sample Step by Step: Adaptive-Consistency for Efficient Reasoning and Coding with LLMs