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BBQ: A hand-built bias benchmark for question answering
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It is well documented that NLP models learn social biases, but little work has been done on how these biases manifest in model outputs for applied tasks like question answering (QA).We introduce the Bias Benchmark for QA (BBQ), a dataset of question sets constructed by the authors that highlight attested social biases against people belonging to protected classes along nine social dimensions relevant for U.S. English-speaking contexts.Our task evaluates model responses at two levels: (i) given an under-informative context, we test how strongly responses refect social biases, and (ii) given an adequately informative context, we test whether the model's biases override a correct answer choice.We fnd that models often rely on stereotypes when the context is under-informative, meaning the model's outputs consistently reproduce harmful biases in this setting.Though models are more accurate when the context provides an informative answer, they still rely on stereotypes and average up to 3.4 percentage points higher accuracy when the correct answer aligns with a social bias than when it conficts, with this difference widening to over 5 points on examples targeting gender for most models tested.
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“LLMs may exhibit social biases and toxicity [37, 53, 153] during the generation process, resulting in the production of biased outputs.”
From A Survey on Evaluation of Large Language Models (2024) · cites this paper as [153]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Evaluating the expanding capabilities of LLMs is a complex research area28,29, with group-specific bias benchmarks shown to be time-consuming to develop and utilize30–33, and the overall field lacking measurement validity and theoretical grounding30,34.”
From Generative language models exhibit social identity biases (2024) · cites this paper as [31]
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“Questions ask for a social group that meets either a negative or positive stereotype, with an ambiguous context that does not contain enough information to answer the question”
From Bias and Fairness in Large Language Models: A Survey (2024) · cites this paper as Parrish et al. 2022
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
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“Bias Benchmark for QA (BBQ) (Parrish et al. 2022) is a questionanswering dataset of 58,492 examples to assess bias across nine social groups: age, disability status, gender, nationality, physical appearance, race/ethnicity, religion, and socioeconomic status.”
From Bias and Fairness in Large Language Models: A Survey (2024) · cites this paper as Parrish et al. 2022
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“Provider documentation indicates that SES does appear in some evaluation suites. For example, BBQ (Bias Benchmark for QA), a standardized test measuring social biases in question-answering, includes SES dimensions [72]. Google's Gemini evaluations separately incorporate Dollar Street, a dataset of household photographs across income levels, for socioeconomic contexts [34, 84].”
From All Public Voices Are Equal, But Are Some More Equal Than Others to LLMs? (2026) · cites this paper as [72]
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“Question-answering models may rely on stereotypes to answer questions in ambiguous contexts (Dhamala et al. 2021; Parrish et al. 2022).”
From Bias and Fairness in Large Language Models: A Survey (2024) · cites this paper as Parrish et al. 2022
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“as well as a disambiguated context that specifies the correct answer.”
From Bias and Fairness in Large Language Models: A Survey (2024) · cites this paper as Parrish et al. 2022
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“The dataset is designed to measure when a model relies on stereotypes.”
From Bias and Fairness in Large Language Models: A Survey (2024) · cites this paper as Parrish et al. 2022
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“LMs have been found to reproduce social biases in downstream tasks, such as textual entailment (Dev et al., 2020; Sharma et al., 2021), language generation (Sheng et al., 2019), and coreference resolution (Rudinger et al., 2018).”
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“Each individual task can be very effort-intensive to implement correctly — as Ganguli et al. (2023) discuss, even a single evaluation such as BBQ (Parrish et al., 2021) can take a significant number of researcher-hours to implement well and understand — which increases the difficulty of vetting all…”
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Metadata
- Authors
- Parrish, A., Chen, A., Nangia, N., Padmakumar, V., Phang, J., Thompson, J., Htut, P. M., & Bowman, S.
- Affiliations
- New York University (US)
- Venue
- Findings of the Association for Computational Linguistics: ACL 2022
- Published
- 2022
- Pages
- 2086-2105
- OpenAlex
- W3207316473
- Cited by
- 186
- References
- 34
- Citations by year
- 2026: 67 · 2025: 62 · 2024: 29 · 2023: 23 · 2022: 5
- FWCI
- 29.56
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Findings of the Association for Computational Linguistics: ACL (Findings of ACL) (publishedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University) (cc-by)
- Funders
- National Science Foundation (2046556, 1922658)
- Samsung
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Ethics and Social Impacts of AI
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Artificial Intelligence › Hate Speech and Cyberbullying Detection
- Keywords
- social bias, question answering, bias benchmark, stereotypes, protected classes, gender bias, model bias evaluation, large language models
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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