The development of Artificial General Intelligence (AGI) has sparked intense interest in creating AI systems that can understand and interact with humans in a more nuanced way. However, most existing benchmarks for evaluating AI capabilities are limited to English and Western cultures, leaving a significant gap in understanding how AI systems perform in diverse cultural contexts. This is where IndQA comes in - a new benchmark designed to evaluate AI systems on Indian culture and languages.
IndQA is a significant step forward in addressing the limitations of current benchmarks, which often focus on translation or multiple-choice tasks. By contrast, IndQA assesses a wide range of culturally relevant topics, including architecture, arts, everyday life, food, history, law, literature, media, religion, and sports. The benchmark consists of 2,278 questions across 12 languages, created in partnership with 261 domain experts from across India.
So, why does IndQA matter? With over 80% of the global population not speaking English as their primary language, it’s essential to develop AI systems that can understand and interact with people from diverse linguistic and cultural backgrounds. IndQA provides a valuable tool for evaluating the performance of AI systems in Indian languages, which will help improve their overall effectiveness and accessibility.
The development of IndQA reflects broader industry trends towards creating more inclusive and culturally sensitive AI systems. By acknowledging the importance of cultural context, IndQA paves the way for more accurate and informative evaluations of AI capabilities. As the AI landscape continues to evolve, benchmarks like IndQA will play a crucial role in shaping the development of more sophisticated and culturally aware AI systems.
How IndQA Works
IndQA uses a rubric-based approach to evaluate AI systems, with each response graded against criteria written by domain experts. The benchmark covers a broad range of topics, including literature, food, and history, with questions written natively in Indian languages. The evaluation process involves a candidate response, a rubric table, and an ideal answer that reflects expert expectations.
Next Steps
The release of IndQA is expected to inspire new benchmark creation from the research community, particularly in languages and cultural domains that are poorly covered by existing AI benchmarks. By creating similar benchmarks, AI research labs can gain a deeper understanding of languages and domains where models struggle, providing a clear direction for future improvements.
Source: Official Link