  Chain-of-Thought (CoT) holds a significant place in augmenting the reasoning
performance for large language models (LLMs). While some studies focus on
improving CoT accuracy through methods like retrieval enhancement, yet a
rigorous explanation for why CoT achieves such success remains unclear. In this
paper, we analyze CoT methods under two different settings by asking the
following questions: (1) For zero-shot CoT, why does prompting the model with
"let's think step by step" significantly impact its outputs? (2) For few-shot
CoT, why does providing examples before questioning the model could
substantially improve its reasoning ability? To answer these questions, we
conduct a top-down explainable analysis from the Hopfieldian view and propose a
Read-and-Control approach for controlling the accuracy of CoT. Through
extensive experiments on seven datasets for three different tasks, we
demonstrate that our framework can decipher the inner workings of CoT, provide
reasoning error localization, and control to come up with the correct reasoning
path.
