We present a systematic study of biases in natural language generation (NLG) by analyzing text generated from prompts that contain mentions of different demographic groups. In this work, we introduce the notion of the regard towards a demographic, use the varying levels of regard towards different demographics as a defining metric for bias in NLG, and analyze the extent to which sentiment scores are a relevant proxy metric for regard. To this end, we collect strategically-generated text from language models and manually annotate the text with both sentiment and regard scores. Additionally, we build an automatic regard classifier through transfer learning, so that we can analyze biases in unseen text. Together, these methods reveal the extent of the biased nature of language model generations. Our analysis provides a study of biases in NLG, bias metrics and correlated human judgments, and empirical evidence on the usefulness of our annotated dataset.

本文通过分析提及不同人群的信息生成的文本，系统研究了自然语言生成中的偏见，并引入了关于群体态度的概念，使用不同群体态度的不同水平作为自然语言生成偏见的定义度量，并分析情感分数在关于群体态度的代理度量方面的相关性。同时，通过收集 strategically-generated text，手动注释文本并构建自动态度分类器来分析未见过的文本中的偏见程度，揭示了语言模型生成的偏见性质。研究提供了关于自然语言生成偏见、偏见度量和相关人类判断的研究以及我们注释的数据集的实证证据。

女子担任保姆工作：语言生成中的偏见