SeeGULL: A Stereotype Benchmark with Broad Geo-Cultural Coverage
  Leveraging Generative Models
Akshita Jha Aida Davani Chandan K. Reddy Shachi Dave Vinodkumar Prabhakaran Sunipa Dev
Stereotype benchmark datasets are crucial to detect and mitigate social
stereotypes about groups of people in NLP models. However, existing datasets
are limited in size and coverage, and are largely restricted to stereotypes
prevalent in the Western society. This is especially problematic as language
technologies gain hold across the globe. To address this gap, we present
SeeGULL, a broad-coverage stereotype dataset, built by utilizing generative
capabilities of large language models such as PaLM, and GPT-3, and leveraging a
globally diverse rater pool to validate the prevalence of those stereotypes in
society. SeeGULL is in English, and contains stereotypes about identity groups
spanning 178 countries across 8 different geo-political regions across 6
continents, as well as state-level identities within the US and India. We also
include fine-grained offensiveness scores for different stereotypes and
demonstrate their global disparities. Furthermore, we include comparative
annotations about the same groups by annotators living in the region vs. those
that are based in North America, and demonstrate that within-region stereotypes
about groups differ from those prevalent in North America. CONTENT WARNING:
This paper contains stereotype examples that may be offensive.