Blind Men and the Elephant: Diverse Perspectives on Gender Stereotypes in Benchmark Datasets (2025.emnlp-main)
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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 . |
| Approach: | They propose that currently available benchmarks capture only partial facets of gender stereotypes . they apply a framework from social psychology to balance data across components of gender stereotypes based on stereotypical benchmarks. |
| Outcome: | The proposed framework improves correlation between different benchmarks by using simple balancing techniques. |
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| Challenge: | Existing benchmarks do not probe professional bias as pronoun resolution may be obfuscated by cross-correlations from other manifestations of gender prejudice. |
| Approach: | They propose to use a skew and stereotype metrics to quantify and analyse the gender bias present in contextual language models when tackling the WinoBias pronoun resolution task. |
| Outcome: | The proposed methods reduce skew and stereotype relative to the unaugmented fine-tuned BERT model. |
CrowS-Pairs: A Challenge Dataset for Measuring Social Biases in Masked Language Models (2020.emnlp-main)
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| Challenge: | Pretrained language models use cultural biases implicitly, causing harm . identifying and quantifying learnt biase enables us to measure progress . |
| Approach: | They propose a benchmark to measure social bias in pretrained language models . they use 1508 examples that cover stereotypes dealing with nine types of bias . |
| Outcome: | The proposed benchmark focuses on stereotypes about historically disadvantaged groups and contrasts them with advantaged groups. |
StereoDetect: Detecting Stereotypes and Anti-stereotypes the Correct Way Using Social Psychological Underpinnings (2025.findings-emnlp)
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| Challenge: | Stereotypes are known to have harmful effects, making their detection critical . current research focuses on detecting and evaluating stereotypical biases . |
| Approach: | They propose a five-tuple definition and provide precise terminologies disentangling stereotypes, antistereotypes, stereotypical bias, and general bias. |
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Your Stereotypical Mileage May Vary: Practical Challenges of Evaluating Biases in Multiple Languages and Cultural Contexts (2024.lrec-main)
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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
| Challenge: | Recent studies have identified a gap in the availability of tools and resources to study bias in languages other than English and social contexts outside the north of America. |
| Approach: | They use stereotypes to build a corpus of sentence pairs that cover biases in seven cultural contexts. |
| Outcome: | The proposed resource covers a wide range of languages and cultural settings . it favors sentences that express stereotypes in most bias categories . |
EuroGEST: Investigating gender stereotypes in multilingual language models (2025.emnlp-main)
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| Challenge: | Large language models encode social biases, but most benchmarks for gender bias remain English-centric. |
| Approach: | They propose a dataset to measure gender-stereotypical reasoning in large language models across English and 29 European languages. |
| Outcome: | The proposed method is highly accurate across languages and strong in translations and gender labels. |
StereoSet: Measuring stereotypical bias in pretrained language models (2021.acl-long)
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| Challenge: | Existing literature on stereotypical biases in language models is limited . current evaluations focus on measuring bias without considering language modeling ability . |
| Approach: | They propose to measure stereotypical biases in four domains: gender, profession, race, and religion . they compare stereotypical and language modeling ability of popular models like BERT, GPT-2, RoBERTa and XLnet . |
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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. |
| Approach: | They apply a measurement modeling lens to inventory pitfalls that threaten benchmarks' validity as measurement models for stereotyping. |
| Outcome: | The proposed benchmarks lack clarity and assumptions that affect how they conceptualize and operationalize stereotyping. |
Intersectional Stereotypes in Large Language Models: Dataset and Analysis (2023.findings-emnlp)
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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 . |
| Approach: | They propose to use a dataset of intersectional stereotypes curated with the ChatGPT model to analyze propagation in three contemporary LLMs. |
| Outcome: | The proposed dataset enables analysis of stereotype propagation in three contemporary LLMs. |
French CrowS-Pairs: Extending a challenge dataset for measuring social bias in masked language models to a language other than English (2022.acl-long)
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| Challenge: | We introduce 1,679 sentence pairs in French that cover stereotypes in ten types of bias like gender and age. |
| Approach: | They build on the US-centered CrowS-pairs dataset to create a multilingual stereotypes dataset that allows for comparability across languages and cultures. |
| Outcome: | The proposed dataset allows for comparability across languages while characterizing biases that are specific to each country and language. |
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. |
| Outcome: | The proposed models validate the findings of the current studies. |