TITLE: BOLD: Dataset and Metrics for Measuring Biases in Open-Ended Language Generation 

ABSTRACT: Recent advances in deep learning techniques have enabled machines to generate
cohesive open-ended text when prompted with a sequence of words as context.
While these models now empower many downstream applications from conversation
bots to automatic storytelling, they have been shown to generate texts that
exhibit social biases. To systematically study and benchmark social biases in
open-ended language generation, we introduce the Bias in Open-Ended Language
Generation Dataset (BOLD), a large-scale dataset that consists of 23,679
English text generation prompts for bias benchmarking across five domains:
profession, gender, race, religion, and political ideology. We also propose new
automated metrics for toxicity, psycholinguistic norms, and text gender
polarity to measure social biases in open-ended text generation from multiple
angles. An examination of text generated from three popular language models
reveals that the majority of these models exhibit a larger social bias than
human-written Wikipedia text across all domains. With these results we
highlight the need to benchmark biases in open-ended language generation and
caution users of language generation models on downstream tasks to be cognizant
of these embedded prejudices.
Recent advances in deep learning techniques have enabled machines to generate
cohesive open-ended text when prompted with a sequence of words as context.
While these models now empower many downstream applications from conversation
bots to automatic storytelling, they have been shown to generate texts that
exhibit social biases. To systematically study and benchmark social biases in
open-ended language generation, we introduce the Bias in Open-Ended Language
Generation Dataset (BOLD), a large-scale dataset that consists of 23,679
English text generation prompts for bias benchmarking across five domains:
profession, gender, race, religion, and political ideology. We also propose new
automated metrics for toxicity, psycholinguistic norms, and text gender
polarity to measure social biases in open-ended text generation from multiple
angles. An examination of text generated from three popular language models
reveals that the majority of these models exhibit a larger social bias than
human-written Wikipedia text across all domains. With these results we
highlight the need to benchmark biases in open-ended language generation and
caution users of language generation models on downstream tasks to be cognizant
of these embedded prejudices.
BOLD: Dataset and Metrics for Measuring Biases in Open-Ended Language Generation
http://arxiv.org/abs/2101.11718v1