Large language models have improved zero-shot text classification by allowing
the transfer of semantic knowledge from the training data in order to classify
among specific label sets in downstream tasks. We propose a simple way to
further improve zero-shot accuracies with minimal effort. We curate small
finetuning datasets intended to describe the labels for a task. Unlike typical
finetuning data, which has texts annotated with labels, our data simply
describes the labels in language, e.g., using a few related terms,
dictionary/encyclopedia entries, and short templates. Across a range of topic
and sentiment datasets, our method is more accurate than zero-shot by 15-17%
absolute. It is also more robust to choices required for zero-shot
classification, such as patterns for prompting the model to classify and
mappings from labels to tokens in the model's vocabulary. Furthermore, since
our data merely describes the labels but does not use input texts, finetuning
on it yields a model that performs strongly on multiple text domains for a
given label set, even improving over few-shot out-of-domain classification in
multiple settings.

本研究提出了一种简单的方法来进一步提高零 - shot 分类准确性，即利用精心策划的微调数据集描述任务的标签，进而在多个文本域中获得强大的预测模型。