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.

该研究旨在在零 - shot 设置中找到高质量的 prompt。我们的自动化方法使用位置、推理和释义技术生成多个与基本 prompt 类似的 prompt，然后使用新的度量标准对这些 prompt 进行排名。我们实验证明，排名靠前的 prompt 是高质量的，显著优于基本 prompt 和使用 few-shot learning 生成的 prompt，适用于句子级情感分类任务。

克服提示扰动敏感性的零样本方法

Zero-shot Approach to Overcome Perturbation Sensitivity of Prompts

Natural-language prompts have recently been used to coax pretrained language
models into performing other AI tasks, using a fill-in-the-blank paradigm
(Petroni et al., 2019) or a few-shot extrapolation paradigm (Brown et al.,
2020). For example, language models retain factual knowledge from their
training corpora that can be extracted by asking them to "fill in the blank" in
a sentential prompt. However, where does this prompt come from? We explore the
idea of learning prompts by gradient descent -- either fine-tuning prompts
taken from previous work, or starting from random initialization. Our prompts
consist of "soft words," i.e., continuous vectors that are not necessarily word
type embeddings from the language model. Furthermore, for each task, we
optimize a mixture of prompts, learning which prompts are most effective and
how to ensemble them. Across multiple English LMs and tasks, our approach
hugely outperforms previous methods, showing that the implicit factual
knowledge in language models was previously underestimated. Moreover, this
knowledge is cheap to elicit: random initialization is nearly as good as
informed initialization.

本文探讨了使用渐进式的梯度下降机制为先前的预训练语言模型 Fine-Tuning 提供含监听词库的句子造句模式，以从中提取模型对于文本特征的认知，并使用软词向量进行任务综合，将此方式与传统的任务方法进行对比，显示其在任务中的巨大性能提升，而随机初始化甚至可以代替详细的初始化方式，因此这种知识的提取可以廉价地实现。