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

• URL: http://arxiv.org/abs/2305.11860v2
• Date: Thu, 16 Nov 2023 16:47:05 GMT
• Title: Let's Sample Step by Step: Adaptive-Consistency for Efficient Reasoning and Coding with LLMs
• Authors: Pranjal Aggarwal, Aman Madaan, Yiming Yang, Mausam
• Abstract summary: A popular approach for improving the correctness of output from large language models (LLMs) is Self-Consistency. We introduce Adaptive-Consistency, a cost-efficient, model-agnostic technique that dynamically adjusts the number of samples per question. Our experiments show that Adaptive-Consistency reduces sample budget by up to 7.9 times with an average accuracy drop of less than 0.1%.
• Score: 60.58434523646137
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: A popular approach for improving the correctness of output from large language models (LLMs) is Self-Consistency - poll the LLM multiple times and output the most frequent solution. Existing Self-Consistency techniques always generate a constant number of samples per question, where a better approach will be to non-uniformly distribute the available budget based on the amount of agreement in the samples generated so far. In response, we introduce Adaptive-Consistency, a cost-efficient, model-agnostic technique that dynamically adjusts the number of samples per question using a lightweight stopping criterion. Our experiments over 17 reasoning and code generation datasets and three LLMs demonstrate that Adaptive-Consistency reduces sample budget by up to 7.9 times with an average accuracy drop of less than 0.1%. Our code and data are available at https://www.sample-step-by-step.info

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