As language models grow in popularity, it becomes increasingly important to
clearly measure all possible markers of demographic identity in order to avoid
perpetuating existing societal harms. Many datasets for measuring bias
currently exist, but they are restricted in their coverage of demographic axes
and are commonly used with preset bias tests that presuppose which types of
biases models can exhibit. In this work, we present a new, more inclusive bias
measurement dataset, HolisticBias, which includes nearly 600 descriptor terms
across 13 different demographic axes. HolisticBias was assembled in a
participatory process including experts and community members with lived
experience of these terms. These descriptors combine with a set of bias
measurement templates to produce over 450,000 unique sentence prompts, which we
use to explore, identify, and reduce novel forms of bias in several generative
models. We demonstrate that HolisticBias is effective at measuring previously
undetectable biases in token likelihoods from language models, as well as in an
offensiveness classifier. We will invite additions and amendments to the
dataset, which we hope will serve as a basis for more easy-to-use and
standardized methods for evaluating bias in NLP models.

本文介绍了一种全新的包含 13 个不同人口统计学轴线近 600 个描述符的、基于 HolisticBias 的更加包容性的偏差测量数据集，结合一组偏差测量模板产生超过 45 万个独特的句子提示，用于探索、识别和减少几个生成模型中的新型偏差。