  Recent studies have demonstrated that natural-language prompts can help to
leverage the knowledge learned by pre-trained language models for the binary
sentence-level sentiment classification task. Specifically, these methods
utilize few-shot learning settings to fine-tune the sentiment classification
model using manual or automatically generated prompts. However, the performance
of these methods is sensitive to the perturbations of the utilized prompts.
Furthermore, these methods depend on a few labeled instances for automatic
prompt generation and prompt ranking. This study aims to find high-quality
prompts for the given task in a zero-shot setting. Given a base prompt, our
proposed approach automatically generates multiple prompts similar to the base
prompt employing positional, reasoning, and paraphrasing techniques and then
ranks the prompts using a novel metric. We empirically demonstrate that the
top-ranked prompts are high-quality and significantly outperform the base
prompt and the prompts generated using few-shot learning for the binary
sentence-level sentiment classification task.
