大型语言模型中的交叉性公平

Computer Science > Computation and Language arXiv:2604.20677 (cs) [Submitted on 22 Apr 2026 (v1), last revised 12 Sep 2026 (this version, v3)] Title:Intersectional Fairness in Large Language Models Authors:Chaima Boufaied, Ronnie De Souza Santos, Ann Barcomb View a PDF of the paper titled Intersectional Fairness in Large Language Models, by Chaima Boufaied and 2 other authors View PDF HTML (experimental) Abstract:Large Language Models (LLMs) are increasingly deployed in socially sensitive settings, raising concerns about fairness and bias, particularly when multiple sensitive attributes intersect. We systematically evaluate intersectional fairness in six LLMs using two datasets from the Bias Benchmark for Question Answering (BBQ), combining race with gender and socioeconomic status. We assess bias, subgroup fairness, accuracy, and consistency across contexts and question polarities. While models perform well in ambiguous contexts, sparse non-unknown predictions limit fairness evaluation. In disambiguated contexts, stereotype alignment affects accuracy differently across datasets: models favor stereotype-reinforcing items in Race-SES but counter-stereotype items in Race-Gender for five of six models. Subgroup fairness metrics reveal uneven outcome distributions despite low observed disparities in some cases, while repeated runs show variable response consistency. Overall, no model consistently achieves both fairness and reliability, highlighting the need to evaluate bias, subgroup fairness, and consistency across intersectional contexts. Subjects: Computation and Language (cs.CL) Cite as: arXiv:2604.20677 [cs.CL] (or arXiv:2604.20677v3 [cs.CL] for this version) https://doi.org/10.48550/arXiv.2604.20677 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Chaima Boufaied [view email] [v1] Wed, 22 Apr 2026 15:25:47 UTC (100 KB) [v2] Thu, 23 Apr 2026 01:37:34 UTC (99 KB) [v3] Sat, 12 Sep 2026 19:28:55 UTC (86 KB) Full-text links: Access Paper: View a PDF of the paper titled Intersectional Fairness in Large Language Models, by Chaima Boufaied and 2 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.CL new | recent | 2026-04 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv (What is alphaXiv?) Links to Code Toggle CatalyzeX Code Finder for Papers (What is CatalyzeX?) DagsHub Toggle DagsHub (What is DagsHub?) GotitPub Toggle Gotit.pub (What is GotitPub?) Huggingface Toggle Hugging Face (What is Huggingface?) ScienceCast Toggle ScienceCast (What is ScienceCast?) Demos Demos Replicate Toggle Replicate (What is Replicate?) Spaces Toggle Hugging Face Spaces (What is Spaces?) Spaces Toggle TXYZ.AI (What is TXYZ.AI?) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower (What are Influence Flowers?) Core recommender toggle CORE Recommender (What is CORE?) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs. Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)