Gender bias in coreference resolution: Evaluation and debiasing methods
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Jieyu Zhao, Tianlu Wang, Mark Yatskar, Vicente Ordonez, Kai-Wei Chang. Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers). 2018.
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Winogender differs from WinoBias by only mentioning one occupation, which interacts with a participant rather than another occupation.
“Winogender also differs from WinoBias by only mentioning one occupation, which instead interacts with a participant, rather than another occupation.”
From Bias and Fairness in Large Language Models: A Survey (2024) · cites this paper as Zhao et al. 2018
Section 2 (WinoBias; page not provided) says that WinoBias pairs entities referred to by different occupations, supporting the WinoBias side of the comparison. Our copy does not describe Winogender or establish that it uses one occupation interacting with a participant (searched: Winogender, participant). (LLM, conf=0.84)
Experimental — accuracy not yet measuredWinogender includes a neutral pronoun option, whereas WinoBias only provides masculine and feminine pronoun genders.
“While WinoBias only provides masculine and feminine pronoun genders, Winogender also includes a neutral option.”
From Bias and Fairness in Large Language Models: A Survey (2024) · cites this paper as Zhao et al. 2018
Section 2, “WinoBias” (page not supplied), says its sentences are duplicated using male and female pronouns, supporting the WinoBias half of the comparison. The passages found do not describe Winogender or establish that it includes a neutral option. (LLM, conf=0.88)
Experimental — accuracy not yet measuredEach WinoBias sentence mentions an interaction between two occupations.
“Each sentence mentions an interaction between two occupations.”
From Bias and Fairness in Large Language Models: A Survey (2024) · cites this paper as Zhao et al. 2018
Winograd schemas have been adapted for bias evaluation to measure words' associations with social groups, most prominently with Winogender and WinoBias.
“Winograd schemas have since been adapted for bias evaluation to measure words' associations with social groups, most prominently with Winogender (Rudinger et al. 2018) and WinoBias (Zhao et al. 2018)”
From Bias and Fairness in Large Language Models: A Survey (2024) · cites this paper as Zhao et al. 2018
Some WinoBias sentences contain no syntactic signals (Type 1), while others are resolvable from syntactic information (Type 2).
“Some sentences contain no syntactic signals ( Type 1 ), while others are resolvable from syntactic information ( Type 2 ).”
From Bias and Fairness in Large Language Models: A Survey (2024) · cites this paper as Zhao et al. 2018
WinoBias measures stereotypical gendered associations with 3,160 sentences over 40 occupations.
“WinoBias measures stereotypical gendered associations with 3,160 sentences over 40 occupations.”
From Bias and Fairness in Large Language Models: A Survey (2024) · cites this paper as Zhao et al. 2018
WinoBias includes sentences that require linking gendered pronouns to stereotypically-associated occupations and sentences that require linking pronouns to anti-stereotypical occupations.
“Some sentences require linking gendered pronouns to their stereotypically-associated occupation, while others require linking pronouns to an anti-stereotypical occupation”
From Bias and Fairness in Large Language Models: A Survey (2024) · cites this paper as Zhao et al. 2018
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“…the contrary) that evaluations, especially more artificially-constructed benchmarks such as minimal-pair or pronoun-frequency (Nangia et al., 2020; Zhao et al., 2018; N´ev´eol et al., 2022) based bias benchmarks or multiple-choice knowledge tests (Hendrycks et al., 2020), provide information…”
- Authors
- Zhao, J., Wang, T., Yatskar, M., Ordonez, V., & Chang, K.-W.
- Affiliations
- University of California, Los Angeles (US)
- University of Virginia (US)
- Allen Institute for Artificial Intelligence (US)
- Venue
- Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)
- Published
- 2018
- Pages
- 15-20
- OpenAlex
- W2963526187
- Cited by
- 684
- References
- 5
- Citations by year
- 2026: 45 · 2025: 92 · 2024: 76 · 2023: 129 · 2022: 96 · 2021: 105 · 2020: 87 · 2019: 48 · 2018: 6
- FWCI
- 24.79
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) (publishedVersion, cc-by)
- Funders
- National Science Foundation (1760523)
- Nvidia
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Keywords
- gender bias, coreference resolution, bias mitigation, evaluation metrics, gender-neutral language
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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