What kinds of instructional prompts are easier to follow for Language Models
(LMs)? We study this question by conducting extensive empirical analysis that
shed light on important features of successful instructional prompts.
Specifically, we study several classes of reframing techniques for manual
reformulation of prompts into more effective ones. Some examples include
decomposing a complex task instruction into multiple simpler tasks or itemizing
instructions into sequential steps. Our experiments compare the zero-shot and
few-shot performance of LMs prompted with reframed instructions on 12 NLP tasks
across 6 categories. Compared with original instructions, our reframed
instructions lead to significant improvements across LMs with different sizes.
For example, the same reframed prompts boost few-shot performance of
GPT3-series and GPT2-series by 12.5% and 6.7% respectively averaged over all
tasks. Furthermore, reframed instructions reduce the number of examples
required to prompt LMs in the few-shot setting. We hope these
empirically-driven techniques will pave the way towards more effective future
prompting algorithms.

通过对 12 个自然语言处理任务的实验比较，研究着手从多个方面进行指导语的重构，以便更好地指导语言模型的零样本和少样本学习，并且比较了各个模型的不同结果，结果表明：重新构建的指导语有助于提高语言模型的学习效果。