SeeGULL: A Stereotype Benchmark with Broad Geo-Cultural Coverage Leveraging Generative Models (2023.acl-long)
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Akshita Jha, Aida Mostafazadeh Davani, Chandan K Reddy, Shachi Dave, Vinodkumar Prabhakaran, Sunipa Dev
| Challenge: | Existing datasets on social stereotypes are limited in size and coverage . existing datasets are restricted to stereotypes prevalent in the Western society . |
| Approach: | They propose a broad-coverage stereotype dataset using generative models and a globally diverse rater pool to validate the prevalence of stereotypes in society. |
| Outcome: | The dataset validates the prevalence of stereotypes in society across 8 geo-political regions across 6 continents and states within the US and India. |
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| Challenge: | Existing studies on intersectional stereotypes focus on broader, individual categories . current studies focus on single-group stereotypes, such as racial bias against African Americans . |
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Stereotyping Norwegian Salmon: An Inventory of Pitfalls in Fairness Benchmark Datasets (2021.acl-long)
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| Challenge: | Several recent efforts have focused on benchmark datasets consisting of pairs of contrastive sentences, which are often accompanied by metrics that aggregate an NLP system’s behavior on these pairs into measurements of harms. |
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Aishwarya Verma, Laud Ammah, Olivia Nercy Ndlovu Lucas, Andrew Zaldivar, Vinodkumar Prabhakaran, Sunipa Dev
| Challenge: | Existing data collection approaches to generative AI are inadequate to assess its safety and utility. |
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Akshita Jha, Vinodkumar Prabhakaran, Remi Denton, Sarah Laszlo, Shachi Dave, Rida Qadri, Chandan Reddy, Sunipa Dev
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| Challenge: | Existing benchmarks for measuring gender stereotypical bias in language models are inconsistencies . lack of explicit standards in data gathering can have detrimental effects on results . |
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Who is better at math, Jenny or Jingzhen? Uncovering Stereotypes in Large Language Models (2024.emnlp-main)
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| Challenge: | Existing research on stereotypes in large language models is limited and focuses on African Ameri- F. |
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Karen Fort, Laura Alonso Alemany, Luciana Benotti, Julien Bezançon, Claudia Borg, Marthese Borg, Yongjian Chen, Fanny Ducel, Yoann Dupont, Guido Ivetta, Zhijian Li, Margot Mieskes, Marco Naguib, Yuyan Qian, Matteo Radaelli, Wolfgang S. Schmeisser-Nieto, Emma Raimundo Schulz, Thiziri Saci, Sarah Saidi, Javier Torroba Marchante, Shilin Xie, Sergio E. Zanotto, Aurélie Névéol
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Scalable and Culturally Specific Stereotype Dataset Construction via Human-LLM Collaboration (2025.emnlp-main)
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| Challenge: | Existing approaches for detecting and mitigating embedded stereotypes rely on carefully annotated datasets like StereoSet and CrowS-Pairs, which are only in English and reflect stereotypes from a few English-speaking countries. Existing datasets, especially translation-based ones, often overlook such cultural distinctions. |
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SHADES: Towards a Multilingual Assessment of Stereotypes in Large Language Models (2025.naacl-long)
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Margaret Mitchell, Giuseppe Attanasio, Ioana Baldini, Miruna Clinciu, Jordan Clive, Pieter Delobelle, Manan Dey, Sil Hamilton, Timm Dill, Jad Doughman, Ritam Dutt, Avijit Ghosh, Jessica Zosa Forde, Carolin Holtermann, Lucie-Aimée Kaffee, Tanmay Laud, Anne Lauscher, Roberto L Lopez-Davila, Maraim Masoud, Nikita Nangia, Anaelia Ovalle, Giada Pistilli, Dragomir Radev, Beatrice Savoldi, Vipul Raheja, Jeremy Qin, Esther Ploeger, Arjun Subramonian, Kaustubh Dhole, Kaiser Sun, Amirbek Djanibekov, Jonibek Mansurov, Kayo Yin, Emilio Villa Cueva, Sagnik Mukherjee, Jerry Huang, Xudong Shen, Jay Gala, Hamdan Al-Ali, null Tair Djanibekov, Nurdaulet Mukhituly, Shangrui Nie, Shanya Sharma, Karolina Stanczak, Eliza Szczechla, Tiago Timponi Torrent, Deepak Tunuguntla, Marcelo Viridiano, Oskar Van Der Wal, Adina Yakefu, Aurélie Névéol, Mike Zhang, Sydney Zink, Zeerak Talat
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Quantifying Stereotypes in Language (2024.eacl-long)
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| Challenge: | Existing studies define a sentence as stereotypical and anti-stereotypical, but they lack a fine-grained quantification of stereotypes. |
| Approach: | They quantify stereotypes in language by annotating a dataset to quantify stereotype of sentences. |
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