Navigating Text-to-Image Generative Bias across Indic Languages

S. Mittal, Arnav Sudan, M. Vatsa, Richa Singh, Tamar Glaser +1 more
2/14/2026

No confident code implementation found for this paper. 2 possible matches can be shown below.

Abstract

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.

View on arXivView PDF

Code Implementations

No confident code match yet

We couldn't find an author-owned or strongly-evidenced community implementation for this paper. 2 weaker matches are hidden by default — verify before relying on them.

No code implementations found yet.

Know of an implementation? Let us know in the comments below!

Similar Papers

IndiBias: A Benchmark Dataset to Measure Social Biases in Language Models for Indian Context

84% match1 repo32024

Decoding the Diversity: A Review of the Indic AI Research Landscape

84% match2024

IndicMMLU-Pro: Benchmarking Indic Large Language Models on Multi-Task Language Understanding

84% match2025

IndicVisionBench: Benchmarking Cultural and Multilingual Understanding in VLMs

84% match2025

IndicGenBench: A Multilingual Benchmark to Evaluate Generation Capabilities of LLMs on Indic Languages

84% match1 repo582024

Evaluating Tokenizer Performance of Large Language Models Across Official Indian Languages

83% match2024

Cite this paper

@article{mittal2026navigating,
  title  = {Navigating Text-to-Image Generative Bias across Indic Languages},
  author = {S. Mittal and Arnav Sudan and M. Vatsa and Richa Singh and Tamar Glaser and Tal Hassner},
  year   = {2026},
  eprint = {2408.00283},
  archivePrefix = {arXiv},
  url    = {https://arxiv.org/abs/2408.00283},
  journal = {ECCV 2024 2024}
}

Add a badge to your README

Shows whether official code is linked for this paper.

Code of Paper badge
[![Code of Paper](https://codeofpaper.com/badge/2408.00283.svg)](https://codeofpaper.com/paper/2408.00283)

Discussion