Contextual Learning of Large Language Models Under Chain-of-Thought Optimization Strategy

Large language models often produce incorrect answers to logical reasoning tasks by relying on memory rather than precise calculations. Generating intermediate reasoning steps can be helpful, but long chains of reasoning can lead to confusion and logical errors. To address this issue, this research focuses on constructing demonstration examples of problem-reasoning chains, introducing correct reasoning paths and logical frameworks, and integrating prior knowledge to guide models in orderly and precise reasoning. This paper innovatively employs active learning strategies, utilizing temperature sampling to obtain multiple reasoning paths, further ensuring the reliability of the answers. Entropy is used to measure answer uncertainty, and clustering analysis is applied to select representative and fair reasoning chain examples that reflect uncertainty. Experiments conducted on five datasets demonstrate that this method effectively optimizes the reasoning chain construction process, enhancing the model's performance in complex logical reasoning and improving the interpretability of the black-box model.