  Program-of-Thought (PoT) replaces natural language-based Chain-of-Thought
(CoT) as the most popular method in Large Language Models (LLMs) mathematical
reasoning tasks by utilizing external tool calls to circumvent computational
errors. However, our evaluation of the GPT-4 and Llama series reveals that
using PoT introduces more reasoning errors, such as incorrect formulas or
flawed logic, compared to CoT. To address this issue, we propose Human-Think
Language (HTL), which leverages a suite of strategies that help integrate PoT
and CoT, encompassing: (1) a new generation paradigm that uses full CoT
reasoning to control code generation. (2) Focus Attention, that directs model
attention to the CoT reasoning during PoT to generate more logical code. (3)
reinforcement learning that utilizes the accuracy of both CoT and PoT responses
as rewards to prevent repetitive reasoning steps in LLMs when solving difficult
math problems. Our method achieves an average improvement of 6.5% on the
Llama-Base model and 4.3% on the Mistral-Base model across 8 mathematical
calculation datasets. It also shows significant effectiveness on five
out-of-domain datasets by controlling the model's information flow, exhibiting
strong transferability. Additionally, HTL shows the most significant
improvement in non-mathematical natural language inference task, contributing
to a unified reasoning task framework
