Measuring South Asian Biases in Large Language Models

• URL: http://arxiv.org/abs/2505.18466v1
• Date: Sat, 24 May 2025 02:18:17 GMT
• Title: Measuring South Asian Biases in Large Language Models
• Authors: Mamnuya Rinki, Chahat Raj, Anjishnu Mukherjee, Ziwei Zhu,
• Abstract summary: This work addresses gaps by conducting a multilingual and intersectional analysis of Large Language Models (LLMs)<n>We construct a culturally grounded bias lexicon capturing previously unexplored intersectional dimensions including gender, religion, marital status, and number of children.<n>We evaluate two self-debiasing strategies to measure their effectiveness in reducing culturally specific bias in Indo-Aryan and Dravidian languages.
• Score: 1.5903891569492878
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Evaluations of Large Language Models (LLMs) often overlook intersectional and culturally specific biases, particularly in underrepresented multilingual regions like South Asia. This work addresses these gaps by conducting a multilingual and intersectional analysis of LLM outputs across 10 Indo-Aryan and Dravidian languages, identifying how cultural stigmas influenced by purdah and patriarchy are reinforced in generative tasks. We construct a culturally grounded bias lexicon capturing previously unexplored intersectional dimensions including gender, religion, marital status, and number of children. We use our lexicon to quantify intersectional bias and the effectiveness of self-debiasing in open-ended generations (e.g., storytelling, hobbies, and to-do lists), where bias manifests subtly and remains largely unexamined in multilingual contexts. Finally, we evaluate two self-debiasing strategies (simple and complex prompts) to measure their effectiveness in reducing culturally specific bias in Indo-Aryan and Dravidian languages. Our approach offers a nuanced lens into cultural bias by introducing a novel bias lexicon and evaluation framework that extends beyond Eurocentric or small-scale multilingual settings.

Related papers

• MyCulture: Exploring Malaysia's Diverse Culture under Low-Resource Language Constraints [7.822567458977689]
  MyCulture is a benchmark designed to comprehensively evaluate Large Language Models (LLMs) on Malaysian culture.<n>Unlike conventional benchmarks, MyCulture employs a novel open-ended multiple-choice question format without predefined options.<n>We analyze structural bias by comparing model performance on structured versus free-form outputs, and assess language bias through multilingual prompt variations.
  arXiv  Detail & Related papers  (2025-08-07T14:17:43Z)
• EuroGEST: Investigating gender stereotypes in multilingual language models [53.88459905621724]
  Large language models increasingly support multiple languages, yet most benchmarks for gender bias remain English-centric.<n>We introduce EuroGEST, a dataset designed to measure gender-stereotypical reasoning in LLMs across English and 29 European languages.
  arXiv  Detail & Related papers  (2025-06-04T11:58:18Z)
• Colombian Waitresses y Jueces canadienses: Gender and Country Biases in Occupation Recommendations from LLMs [15.783346695504344]
  We study the first study of multilingual intersecting country and gender biases.<n>We construct a benchmark of prompts in English, Spanish and German, using 25 countries and four pronoun sets.<n>We find that even when models show parity for gender or country individually, intersectional occupational biases based on both country and gender persist.
  arXiv  Detail & Related papers  (2025-05-05T08:40:51Z)
• CARE: Aligning Language Models for Regional Cultural Awareness [28.676469530858924]
  Existing language models (LMs) often exhibit a Western-centric bias and struggle to represent diverse cultural knowledge.<n>Previous attempts to address this rely on synthetic data and express cultural knowledge only in English.<n>We first introduce CARE, a multilingual resource of 24.1k responses with human preferences on 2,580 questions about Chinese and Arab cultures.
  arXiv  Detail & Related papers  (2025-04-07T14:57:06Z)
• Assessing Agentic Large Language Models in Multilingual National Bias [31.67058518564021]
  Cross-language disparities in reasoning-based recommendations remain largely unexplored.<n>This study is the first to address this gap.<n>We investigate multilingual bias in state-of-the-art LLMs by analyzing their responses to decision-making tasks across multiple languages.
  arXiv  Detail & Related papers  (2025-02-25T08:07:42Z)
• See It from My Perspective: How Language Affects Cultural Bias in Image Understanding [60.70852566256668]
  Vision-language models (VLMs) can respond to queries about images in many languages.<n>We characterize the Western bias of VLMs in image understanding and investigate the role that language plays in this disparity.
  arXiv  Detail & Related papers  (2024-06-17T15:49:51Z)
• White Men Lead, Black Women Help? Benchmarking Language Agency Social Biases in LLMs [58.27353205269664]
  Social biases can manifest in language agency. We introduce the novel Language Agency Bias Evaluation benchmark. We unveil language agency social biases in 3 recent Large Language Model (LLM)-generated content.
  arXiv  Detail & Related papers  (2024-04-16T12:27:54Z)
• Multilingual Text-to-Image Generation Magnifies Gender Stereotypes and Prompt Engineering May Not Help You [64.74707085021858]
  We show that multilingual models suffer from significant gender biases just as monolingual models do. We propose a novel benchmark, MAGBIG, intended to foster research on gender bias in multilingual models. Our results show that not only do models exhibit strong gender biases but they also behave differently across languages.
  arXiv  Detail & Related papers  (2024-01-29T12:02:28Z)
• Global Voices, Local Biases: Socio-Cultural Prejudices across Languages [22.92083941222383]
  Human biases are ubiquitous but not uniform; disparities exist across linguistic, cultural, and societal borders. In this work, we scale the Word Embedding Association Test (WEAT) to 24 languages, enabling broader studies. To encompass more widely prevalent societal biases, we examine new bias dimensions across toxicity, ableism, and more.
  arXiv  Detail & Related papers  (2023-10-26T17:07:50Z)
• Quantifying the Dialect Gap and its Correlates Across Languages [69.18461982439031]
  This work will lay the foundation for furthering the field of dialectal NLP by laying out evident disparities and identifying possible pathways for addressing them through mindful data collection.
  arXiv  Detail & Related papers  (2023-10-23T17:42:01Z)
• Comparing Biases and the Impact of Multilingual Training across Multiple Languages [70.84047257764405]
  We present a bias analysis across Italian, Chinese, English, Hebrew, and Spanish on the downstream sentiment analysis task. We adapt existing sentiment bias templates in English to Italian, Chinese, Hebrew, and Spanish for four attributes: race, religion, nationality, and gender. Our results reveal similarities in bias expression such as favoritism of groups that are dominant in each language's culture.
  arXiv  Detail & Related papers  (2023-05-18T18:15:07Z)
• An Analysis of Social Biases Present in BERT Variants Across Multiple Languages [0.0]
  We investigate the bias present in monolingual BERT models across a diverse set of languages. We propose a template-based method to measure any kind of bias, based on sentence pseudo-likelihood. We conclude that current methods of probing for bias are highly language-dependent.
  arXiv  Detail & Related papers  (2022-11-25T23:38:08Z)
• Gender Bias in Multilingual Embeddings and Cross-Lingual Transfer [101.58431011820755]
  We study gender bias in multilingual embeddings and how it affects transfer learning for NLP applications. We create a multilingual dataset for bias analysis and propose several ways for quantifying bias in multilingual representations.
  arXiv  Detail & Related papers  (2020-05-02T04:34:37Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.