  Large Language Models prompting, such as using in-context demonstrations, is
a mainstream technique for invoking LLMs to perform high-performance and solid
complex reasoning (e.g., mathematical reasoning, commonsense reasoning), and
has the potential for further human-machine collaborative scientific findings.
However, current LLMs are delicate and elusive in prompt words and styles. And
there is an unseen gap between LLM understanding and human-written prompts.
This paper introduces AlignedCoT, an LLM-acquainted prompting technique that
includes proficient "native-speaking" in in-context learning for the LLMs.
Specifically, it achieves consistent and correct step-wise prompts in zero-shot
scenarios by progressively probing, refining, and formatting the LLM chain of
thoughts so that free from handcrafted few-shot demonstrations while
maintaining the prompt quality. We conduct experiments on mathematical
reasoning and commonsense reasoning. We find that LLMs with AlignedCoT perform
significantly superior to them with human-crafted demonstrations. We further
apply AlignedCoT for rewriting the GSM8k training set, resulting in a
GSM8k-Align dataset. We observe its benefits for retrieval augmented
generation.
