HONEST: Measuring Hurtful Sentence Completion in Language Models

Debora Nozza, Federico Bianchi, Dirk Hovy


Abstract
Language models have revolutionized the field of NLP. However, language models capture and proliferate hurtful stereotypes, especially in text generation. Our results show that 4.3% of the time, language models complete a sentence with a hurtful word. These cases are not random, but follow language and gender-specific patterns. We propose a score to measure hurtful sentence completions in language models (HONEST). It uses a systematic template- and lexicon-based bias evaluation methodology for six languages. Our findings suggest that these models replicate and amplify deep-seated societal stereotypes about gender roles. Sentence completions refer to sexual promiscuity when the target is female in 9% of the time, and in 4% to homosexuality when the target is male. The results raise questions about the use of these models in production settings.
Anthology ID:
2021.naacl-main.191
Volume:
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
Month:
June
Year:
2021
Address:
Online
Editors:
Kristina Toutanova, Anna Rumshisky, Luke Zettlemoyer, Dilek Hakkani-Tur, Iz Beltagy, Steven Bethard, Ryan Cotterell, Tanmoy Chakraborty, Yichao Zhou
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
2398–2406
Language:
URL:
https://aclanthology.org/2021.naacl-main.191
DOI:
10.18653/v1/2021.naacl-main.191
Bibkey:
Cite (ACL):
Debora Nozza, Federico Bianchi, and Dirk Hovy. 2021. HONEST: Measuring Hurtful Sentence Completion in Language Models. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 2398–2406, Online. Association for Computational Linguistics.
Cite (Informal):
HONEST: Measuring Hurtful Sentence Completion in Language Models (Nozza et al., NAACL 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.naacl-main.191.pdf
Video:
 https://aclanthology.org/2021.naacl-main.191.mp4
Data
HONEST