In a major strategic move to enhance the multilingual and multicultural capabilities of its technology, OpenAI has officially introduced “IndQA.” This pioneering benchmark is meticulously designed to test how effectively artificial intelligence (AI) systems can comprehend and navigate the complex tapestry of India’s diverse languages, cultural nuances, and regional contexts.
The initiative marks a significant step in the global push for more inclusive AI, starting with one of the world’s most linguistically rich regions. With India standing as ChatGPT’s second-largest market, this move underscores OpenAI’s commitment to making its technology more reliable and attuned for non-English users.
A New Standard for Cultural Authenticity
Developed in close collaboration with a diverse group of 261 domain experts from across India, IndQA is a comprehensive and robust dataset. It comprises 2,278 high-quality questions that are not only challenging but also deeply embedded in the Indian context.
What truly sets IndQA apart from conventional benchmarks like MMMLU and MGSM is its development process. OpenAI states that the content is “natively written”—meaning it was conceptualized and written directly in the local languages by experts, not created in English and then translated.
This “natively written” approach is critical. It ensures that the phrasing, intent, and cultural context of each question are authentic, testing an AI’s genuine understanding of subtle nuances, idioms, and region-specific knowledge, rather than just its ability to process literal translations.
How IndQA Works: Beyond Multiple Choice
IndQA also introduces a more sophisticated evaluation method. It moves away from simple multiple-choice testing and adopts a “rubric-based evaluation system.”
Here’s the process:
- The Prompt: Each question includes a culturally contextual prompt written in one of the 12 Indian languages.
- Verification: An English translation is provided alongside the prompt, purely for verification and clarity for a global team.
- The Rubric: A detailed grading rubric, created by the domain experts, accompanies each question.
- The Ideal Answer: An expert-level, ideal answer is provided as a gold standard.
AI models are then assessed against the specific criteria within the rubric, which carry weighted scores. This allows for a far more granular evaluation of an AI’s performance, grading it on its grasp of nuance, its reasoning capabilities, and its cultural correctness—not just a simple right or wrong.
Comprehensive Linguistic and Cultural Scope
The IndQA benchmark is ambitious in its scope, covering a significant portion of India’s linguistic and cultural landscape.
- 12 Languages: The dataset covers Bengali, English, Hindi, Hinglish, Kannada, Marathi, Odia, Telugu, Gujarati, Malayalam, Punjabi, and Tamil.
- 10 Cultural Domains: The questions are drawn from a wide array of cultural and intellectual areas, including:
- Architecture & Design
- Arts & Culture
- Everyday Life
- Law & Ethics
- Media & Entertainment
- Religion & Spirituality
- Sports & Recreation
- And other core areas of Indian life.
OpenAI has already begun benchmarking its most advanced models, including GPT-4o, OpenAI o3, GPT-4.5, and the anticipated GPT-5, against this new standard to measure and improve their performance.
IndQA: At a Glance
- Total Questions: 2,278
- Collaborators: 261 domain experts from India
- Languages: 12 (including Hindi, Tamil, Bengali, Telugu, etc.)
- Cultural Domains: 10 (including Arts, History, Law, Daily Life)
- Evaluation Method: Rubric-based grading (not multiple-choice)
- Key Feature: “Natively written” content, not translated
The Future of Inclusive AI
India was strategically chosen as the starting point for this project, not only because of its market size but because nearly a billion Indians do not use English as their primary language.
Srinivas Narayanan, CTO of B2B Applications at OpenAI, emphasized the project’s goal, stating that the aim was to ensure models grasp “the nuances every culture cares about.”
The launch of IndQA is not an endpoint. OpenAI has stated that it plans to replicate this comprehensive framework in other regions and for other cultures, using the lessons learned from the IndQA project. This signals a clear and dedicated effort to build AI systems that understand people the way they naturally speak and think, regardless of their language, ultimately fostering a more inclusive and accessible AI for the entire world.
