Let’s Sample Step by Step: Adaptive-Consistency for Efficient Reasoning and Coding with LLMs (2023.emnlp-main)
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| Challenge: | Existing methods for improving the correctness of output from large language models generate a constant number of samples per question, but Adaptive-Consistency reduces sample budget by up to 7.9 times with an average accuracy drop of less than 0.1%. |
| Approach: | They propose a model-agnostic technique that dynamically adjusts the number of samples per question using a lightweight stopping criterion. |
| Outcome: | The proposed technique reduces sample budget by 7.9 times with an average accuracy drop of less than 0.1%. |
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