Enhancing Mathematical Reasoning in LLMs by Stepwise Correction
Enhancing Mathematical Reasoning in LLMs by Stepwise Correction
Best-of-N decoding methods instruct large language models (LLMs) to generate multiple solutions, score each using a scoring function, and select the highest scored as the final answer to mathematical reasoning problems. However, this repeated independent process often leads to the same mistakes, making the selected solution still incorrect. We propose …