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cs.CL2024
Enhancing Mathematical Reasoning in LLMs by Stepwise Correction
Zhenyu Wu, Qingkai Zeng, Zhihan Zhang +3
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 ans…
cs.CL2024
Large Language Models Can Self-Correct with Key Condition Verification
Zhenyu Wu, Qingkai Zeng, Zhihan Zhang +3
Intrinsic self-correct was a method that instructed large language models (LLMs) to verify and correct their responses without external feedback. Unfortunately, the study concluded…
cs.CL2024
Instructing Large Language Models to Identify and Ignore Irrelevant Conditions
Zhenyu Wu, Chao Shen, Meng Jiang
Math word problem (MWP) solving requires generating a reasoning path based on a given problem description that often contains irrelevant conditions. Existing chain-of-thought (CoT)…