🔗 Who cites «Linguistic Bias in ChatGPT: Language Models Reinforce Dialect Discrimination»

36 papers of our library cite it (of the 149,439 whose bibliographies we read) · 16 with a page here, the most cited first · ← back to «Linguistic Bias in ChatGPT: Language Models Reinforce Dialect Discrimination»

1. Generative AI and linguistic diversity in academic writing and publishing: Perspectives from World EnglishesarXiv:2607.28505 · Generative AI and linguistic diversity in academic writing and publishing: Perspectives from World Englishes. Journal of English for Research Publication Purposes, 2025, 6(2), 265-289Kingsley Ugwuanyi, Christian Mair, Sender Dovchin, et al.2025 · 4 citations
2. Finding A Voice: Exploring the Potential of African American Dialect and Voice Generation for ChatbotsAnnual Meeting of the Association for Computational Linguistics (ACL)Sarah E. Finch, Ellie S. Paek, Ikseon Choi, et al.2025 · 3 citations
3. Trans-EnV: A Framework for Evaluating the Linguistic Robustness of LLMs Against English VarietiesConference on Neural Information Processing Systems (NeurIPS)Jiyoung Lee, Seungho Kim, Jieun Han, et al.2025 · 3 citations
4. AI systems and the reproduction of (standard) language ideologies in World EnglishesarXiv:2607.28528Kingsley Ugwuanyi2026
5. An Investigation of Linguistic Biases in LLM-Based RecommendationsarXiv:2604.25456Nitin Venkateswaran, Jason Ang, Deep Adhikari, et al.2026
6. Can I guess where you are from? Modeling dialectal morphosyntactic similarities in Brazilian PortugueseInternational Conference on Computational Processing of PortugueseManoel Siqueira, Raquel Freitag2026
7. DialectLLM: A Dialect-Aware Dialog[ue] Generation Framework Beyond Standard American EnglisharXiv:2601.22888Jio Oh, Paul Vicinanza, Thomas Butler, et al.2026
8. DiaLLM: An Investigation into the Robustness-Generation Gap in English Dialect AdaptationarXiv:2607.07669Jordan Painter, Dipankar Srirag, Adarsh Kappiyath, et al.2026
9. Evaluating the Usage of African-American Vernacular English in Large Language ModelsarXiv:2602.21485Deja Dunlap, R. Thomas McCoy2026
10. From a River in Gilead to the Inference Distributions of Large Language Models: Covert Dialect Bias and Linguistic Profiling at ScaleProceedings of the AAAI/ACM Conference on AI Ethics and SocietyChowdhury Mohammad Abdullah, Rita Orji2026
11. How Does "English (US)" Become the Default? Triangulating Structural Bias Towards American English Across the LLM PipelinearXiv:2604.04204Mir Tafseer Nayeem, Davood Rafiei2026
12. LLMs Silently Correct African American English: Auditing and Mitigating Dialect Bias via Activation SteeringarXiv:2607.06845Huan Wu, Ali Emami, Muhammad Furquan Hassan, et al.2026
13. Side-by-side Comparison Amplifies Dialect Bias in Language ModelsACM Conference on Fairness, Accountability, and Transparency (FAccT)Kritee Kondapally, Claire J. Smerdon, Pooja C. Patel, et al.2026
14. Some Dialects Are More Equal Than Others: Non-Prestigious Arabic Dialectal Bias in LLMsarXiv:2609.23955Mai Mohamed Eida, Ryan Dolan, Paul de Nijs, et al.2026
15. When the Gold Standard Isn't Necessarily Standard: Challenges of Evaluating the Translation of User-Generated ContentAnnual Conference of the European Association for Machine Translation (EAMT)Lydia Nishimwe, Benoît Sagot, Rachel Bawden2026
16. Social Perceptions of English Spelling Variation on Twitter: A Comparative Analysis of Human and LLM ResponsesarXiv:2511.23041Dong Nguyen, Laura Rosseel2025

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