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Exploring Gender Bias Beyond Occupational Titles

arXiv 2507.02679 ↗First posted 3 Jul 2025cs.CLcited by 0

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Abstract (arXiv)

In this work, we investigate the correlation between gender and contextual biases, focusing on elements such as action verbs, object nouns, and particularly on occupations. We introduce a novel dataset, GenderLexicon, and a framework that can estimate contextual bias and its related gender bias. Our model can interpret the bias with a score and thus improve the explainability of gender bias. Also, our findings confirm the existence of gender biases beyond occupational stereotypes. To validate our approach and demonstrate its effectiveness, we conduct evaluations on five diverse datasets, including a Japanese dataset.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 23 of its 44 references that are in the map. First posted on arXiv 3 Jul 2025.

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References · 23

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11 more
  1. 2017Men Also Like Shopping: Reducing Gender Bias Amplification using Corpus-level Constraintscited by 420
  2. 2018Learning Word Vectors for 157 Languagescited by 279
  3. 2019Evaluating Gender Bias in Machine Translationcited by 247
  4. 2020Intrinsic Bias Metrics Do Not Correlate with Application Biascited by 148
  5. 2021Collecting a Large-Scale Gender Bias Dataset for Coreference Resolution and Machine Translationcited by 62
  6. 2022Gender Bias in Masked Language Models for Multiple Languagescited by 60
  7. 2024Gender Bias in Large Language Models across Multiple Languagescited by 35
  8. 2019Good Secretaries, Bad Truck Drivers? Occupational Gender Stereotypes in Sentiment Analysiscited by 19
  9. 2022Sense Embeddings are also Biased--Evaluating Social Biases in Static and Contextualised Sense Embeddingscited by 18
  10. 2024RakutenAI-7B: Extending Large Language Models for Japanesecited by 13
  11. 2023BiasTestGPT: Using ChatGPT for Social Bias Testing of Language Modelscited by 8

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