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Chain-of-Thought (CoT) prompting methods have enabled large language models(LLMs) to generate reasoning paths and solve math word problems (MWPs).However, they are sensitive to mistakes in the paths, as any mistake can resultin an incorrect answer. We propose a novel method named ProgressiveRectification Prompting (PRP) to improve average accuracy on eight MWP datasetsfrom 77.3 to 90.5. Given an initial answer from CoT, PRP iterates averify-then-rectify process to progressively identify incorrect answers andrectify the reasoning paths. With the most likely correct answer, the LLMpredicts a masked numerical value in the question; if the prediction does notmatch the masked value, the answer is likely incorrect. Then the LLM isprompted to re-generate the reasoning path hinted with a set of incorrectanswers to prevent itself from repeating previous mistakes. PRP achieves thebest performance compared against the CoT methods. Our implementation is madepublicly available at https://wzy6642.github.io/prp.github.io/.

Get an A in Math: Progressive Rectification Prompting