mlsanityData to 14 Oct 2026

Navigating Text-to-Image Generative Bias across Indic Languages

arXiv 2408.00283 ↗First posted 1 Aug 2024cs.CL cs.CVcited by 5

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Abstract (arXiv)

This research investigates biases in text-to-image (TTI) models for the Indic languages widely spoken across India. It evaluates and compares the generative performance and cultural relevance of leading TTI models in these languages against their performance in English. Using the proposed IndicTTI benchmark, we comprehensively assess the performance of 30 Indic languages with two open-source diffusion models and two commercial generation APIs. The primary objective of this benchmark is to evaluate the support for Indic languages in these models and identify areas needing improvement. Given the linguistic diversity of 30 languages spoken by over 1.4 billion people, this benchmark aims to provide a detailed and insightful analysis of TTI models' effectiveness within the Indic linguistic landscape. The data and code for the IndicTTI benchmark can be accessed at https://iab-rubric.org/resources/other-databases/indictti.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 14 of its 16 references that are in the map. First posted on arXiv 1 Aug 2024.

Builds on

14 of 16 references.

References · 14

  1. 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,863
  2. 2021Zero-Shot Text-to-Image Generationcited by 4,288
  3. 2022LAION-5B: An open large-scale dataset for training next generation image-text modelscited by 4,152
  4. 2022No Language Left Behind: Scaling Human-Centered Machine Translationcited by 1,312
  5. 2023MEGA: Multilingual Evaluation of Generative AIcited by 293
  6. 2023IndicTrans2: Towards High-Quality and Accessible Machine Translation Models for all 22 Scheduled Indian Languagescited by 152
  7. 2022AltCLIP: Altering the Language Encoder in CLIP for Extended Language Capabilitiescited by 85
  8. 2022Exploiting Cultural Biases via Homoglyphs in Text-to-Image Synthesiscited by 29
  9. 2023NLLB-CLIP -- train performant multilingual image retrieval model on a budgetcited by 24
  10. 2024Would Deep Generative Models Amplify Bias in Future Models?cited by 24
  11. 2024Multilingual Text-to-Image Generation Magnifies Gender Stereotypes and Prompt Engineering May Not Help Youcited by 15
  12. 2023On the Cultural Gap in Text-to-Image Generationcited by 10
2 more
  1. 2023Translation-Enhanced Multilingual Text-to-Image Generationcited by 9
  2. 2023Multilingual Conceptual Coverage in Text-to-Image Modelscited by 9

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 1; 2025: 3; 2026: 1.

5 papers.

  1. 2024Survey of Cultural Awareness in Language Models: Text and Beyondcited by 103
  2. 2025TALES: A Taxonomy and Analysis of Cultural Representations in LLM-generated Storiescited by 10
  3. 2025Evaluation of Cultural Competence of Vision-Language Modelscited by 3
  4. 2025From No to Know: Taxonomy, Challenges, and Opportunities for Negation Understanding in Multimodal Foundation Modelscited by 1
  5. 2026On the Limitations of Cross-Lingual Consistency in Multilingual Text-to-image Generationcited by 1

Similar, not cited

Close in wording, no citation either way

  1. 2025IndicVisionBench: Benchmarking Cultural and Multilingual Understanding in VLMs
  2. 2025Deconstructing Bias: A Multifaceted Framework for Diagnosing Cultural and Compositional Inequities in Text-to-Image Generative Models
  3. 2025Exposing Blindspots: Cultural Bias Evaluation in Generative Image Models
  4. 2026T2I-BiasBench: A Multi-Metric Framework for Auditing Demographic and Cultural Bias in Text-to-Image Models
  5. 2024BIGbench: A Unified Benchmark for Evaluating Multi-dimensional Social Biases in Text-to-Image Models
  6. 2025IndicRAGSuite: Large-Scale Datasets and a Benchmark for Indian Language RAG Systems
  7. 2026BAFIS: Dataset + Framework to assess occupational Bias and Human Preference in modern Text-to-image Models
  8. 2025Multimodal Language Models as Text-to-Image Model Evaluators