mlsanityData to 14 Oct 2026

'Since Lawyers are Males..': Examining Implicit Gender Bias in Hindi Language Generation by LLMs

arXiv 2409.13484 ↗First posted 20 Sep 2024cs.CL cs.AI cs.HCcited by 8

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Abstract (arXiv)

Large Language Models (LLMs) are increasingly being used to generate text across various languages, for tasks such as translation, customer support, and education. Despite these advancements, LLMs show notable gender biases in English, which become even more pronounced when generating content in relatively underrepresented languages like Hindi. This study explores implicit gender biases in Hindi text generation and compares them to those in English. We developed Hindi datasets inspired by WinoBias to examine stereotypical patterns in responses from models like GPT-4o and Claude-3 sonnet. Our results reveal a significant gender bias of 87.8% in Hindi, compared to 33.4% in English GPT-4o generation, with Hindi responses frequently relying on gender stereotypes related to occupations, power hierarchies, and social class. This research underscores the variation in gender biases across languages and provides considerations for navigating these biases in generative AI systems.

Key facts. Cited by 8 papers in the map (data through 14 Oct 2026). Builds on 59 of its 113 references that are in the map. First posted on arXiv 20 Sep 2024.

Builds on

59 of 113 references.

References · 59

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38 more
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50 most cited of 59 All 59 (JSON)

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 2; 2026: 6.

8 papers.

  1. 2025FairI Tales: Evaluation of Fairness in Indian Contexts with a Focus on Bias and Stereotypescited by 9
  2. 2025Large Language Models Develop Novel Social Biases Through Adaptive Explorationcited by 2
  3. 2026Dual-Metric Evaluation of Social Bias in Large Language Models: Evidence from an Underrepresented Nepali Cultural Contextcited by 1
  4. 2026BhashaSutra: A Task-Centric Unified Survey of Indian NLP Datasets, Corpora, and Resourcescited by 1
  5. 2026Personality Shapes Gender Bias in Persona-Conditioned LLM Narratives Across English and Hindi: An Empirical Investigationcited by 1
  6. 2026MORPHOGEN: A Multilingual Benchmark for Evaluating Gender-Aware Morphological Generationcited by 0
  7. 2026LLMs Mirror Country-Specific Gender Patterns If Asked, but Skew Male When Generating Media in Local Languagescited by 0
  8. 2026When Who You Are Can Change the Code You Get: A Study of Persona-Induced Bias in LLM Code Generationcited by 0