Speak Like a Native: Prompting Large Language Models in a Native Style.
CoRR(2023)
摘要
Existing work has found that the prompt engineering heavily influences the
performance of large language models (LLMs). Chain-of-thought (CoT), as a
popular prompt engineering technique, prompted LLMs using in-context examples
with reasoning steps. In current studies, the few-shot examples of CoT are
generally handcrafted by humans. However, how the text style of in-context
examples influence the outputs of LLMs still remains under-explored. This paper
presents a novel and effective approach, named \textbf{AlignCoT}, to improve
the reasoning capability of LLMs by aligning the in-context examples with the
native style of LLMs. ``Native'' refers to the inherent characteristic style of
LLMs which can be probed by original zero-shot scenarios. AlignCoT is
orthogonal to other prompt engineering methods, making it easy to combine with
state-of-the-art techniques to further improve the LLMs' performance. We
conduct extensive and comprehensive experiments on several benchmarks. The
empirical results demonstrate that our AlignCoTsignificantly improves
performance over the carefully handcrafted in-context examples. For instance,
with GPT-3.5-turbo, we observed a +2.5\% improvement on GSM8K. Furthermore, our
AlignCoT consistently improve the performance when combined with other
state-of-the-art prompt engineering methods. The source code and dataset will
be available at
\href{https://github.com/yangzhch6/AlignCoT}{https://github.com/yangzhch6/AlignCoT}.
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