Language model (LM) prompting--a popular paradigm for solving NLP tasks--has been shown to be susceptible to miscalibration and brittleness to slight prompt variations, caused by its discriminative prompting approach, i.e., predicting the label given the input. To address these issues, we propose Gen-Z--a generative prompting framework for zero-shot text classification. GEN-Z is generative, as it measures the LM likelihood of input text, conditioned on natural language descriptions of labels. The framework is multivariate, as label descriptions allow us to seamlessly integrate additional contextual information about the labels to improve task performance. On various standard classification benchmarks, with six open-source LM families, we show that zero-shot classification with simple contextualization of the data source of the evaluation set consistently outperforms both zero-shot and few-shot baselines while improving robustness to prompt variations. Further, our approach enables personalizing classification in a zero-shot manner by incorporating author, subject, or reader information in the label descriptions.

翻译：语言模型提示——一种流行的自然语言处理任务解决范式——已被证明易出现校准偏差且对细微提示变化敏感，其根源在于判别式提示方法（即根据输入预测标签）。为解决这些问题，我们提出Gen-Z——一种面向零样本文本分类的生成式提示框架。GEN-Z采用生成式方法，通过测量输入文本在标签自然语言描述条件下的语言模型似然度。该框架具有多变量特性，因标签描述使我们能无缝整合标签的额外上下文信息以提升任务性能。在多个标准分类基准上，使用六个开源语言模型家族的实验表明：通过简单地对评估集数据源进行上下文化的零样本分类，其表现始终优于零样本和少样本基线方法，同时提升了对提示变化的鲁棒性。此外，我们的方法通过将作者、主题或读者信息纳入标签描述，实现了零样本方式下的个性化分类。