| Challenge: | a new study quantitatively evaluates gender stereotypes in written language . female writings contain fewer gender stereotype scores than male writings . |
| Approach: | They quantitatively evaluate and analyze gender stereotypes in written language . they compare writings by female authors with writings from male authors . |
| Outcome: | The results show that writings by female authors have lower gender stereotype scores . the authors plan on using more datasets over the past century to study gender stereotypes . |
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| Challenge: | In this paper, we demonstrate that there are large-scale differences in the ways that people talk about women and men and that these differences vary across domains. |
| Approach: | They propose to integrate two datasets and a novel approach to automatically infer gender associations from language and find coherent word clusters and label clusters for the semantic concepts they represent. |
| Outcome: | The proposed methods outperform strong baselines in large-scale studies of how people talk about women and men in two different settings. |
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. |
Exploring Human Gender Stereotypes with Word Association Test (D19-1)
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| Challenge: | Existing word embeddings have been used to study gender stereotypes in texts . however, evaluating their validities is still an open problem . et al.: this study investigates gender bias using the lens of language, especially, the words . |
| Approach: | They use word association test to derive bias scores for large amount of words . they find that these bias scores correlate well with bias in the real world . |
| Outcome: | The proposed method correlates well with bias in the real world, and with census data, it provides a different perspective on gender stereotypes in words. |
LLMs Reproduce Stereotypes of Sexual and Gender Minorities (2025.findings-emnlp)
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| Challenge: | a large body of research has found substantial gender bias in NLP systems . authors show that LLMs generate stereotyped representations of sexual and gender minorities in this setting . |
| Approach: | They propose to use a stereotype content model to study gender bias in large language models . they show that LLMs generate stereotyped representations of sexual and gender minorities . |
| Outcome: | The proposed model generates negative stereotypes of sexual and gender minorities in English-language surveys . |
Tales and Tropes: Gender Roles from Word Embeddings in a Century of Children’s Books (2022.coling-1)
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Anjali Adukia, Patricia Chiril, Callista Christ, Anjali Das, Alex Eble, Emileigh Harrison, Hakizumwami Birali Runesha
| Challenge: | In 100 years of influential children's books, gender is portrayed in a way that reproduces traditional gender norms in society. |
| Approach: | They use word embeddings to train a model to detect individual sentences containing stereotypes to measure how gender is portrayed in children's books. |
| Outcome: | The proposed model trains a model to detect individual sentences containing stereotypes to gain a deeper understanding of the messages conveyed to children by the books they read. |
Examining Gender Bias in Languages with Grammatical Gender (D19-1)
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| Challenge: | Existing studies on gender bias in word embeddings focus on English . however, these studies cannot be extended to languages with morphological agreement on gender . |
| Approach: | They propose new metrics to evaluate gender bias in word embeddings of English and Spanish . they extend existing approaches to mitigate gender bias while preserving original embeddables . |
| Outcome: | The proposed methods reduce gender bias while preserving the original embeddings. |
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. |
Revisiting the Classics: A Study on Identifying and Rectifying Gender Stereotypes in Rhymes and Poems (2024.lrec-main)
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| Challenge: | This study highlights the pervasive existence of gender stereotypes in literary works and proposes a model with 97% accuracy to identify gender bias. |
| Approach: | They propose a large language model with 97% accuracy to identify gender bias in rhymes and poems and a model with a comparative survey against human educator rectifications. |
| Outcome: | The proposed model has 97% accuracy and can be used to identify gender biases in rhymes and poems. |
Unsupervised Discovery of Gendered Language through Latent-Variable Modeling (P19-1)
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| Challenge: | a recent study has focused on the ways in which language is gendered . positive adjectives used to describe women are more often related to their bodies . |
| Approach: | They propose a model that models adjective choice and its sentiment given the natural gender of a head noun. |
| Outcome: | The proposed model shows that positive adjectives used to describe women are more often related to their bodies than positive adjective words used to explain men. |
Robustness and Reliability of Gender Bias Assessment in Word Embeddings: The Role of Base Pairs (2020.aacl-main)
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| Challenge: | Existing methods to quantify gender bias in word embeddings are not robust and cannot identify common types of bias. |
| Approach: | They propose to quantify gender bias by using cosine similarity to a pair of gender words and using analogies. |
| Outcome: | The proposed methods are not robust and cannot identify common types of bias, while analogies are unsuitable indicators. |