How can we reliably assess whether the major linguistic models actually understand the Indian languages and cultures of the real world tribes? Openai released DOG, Benchmarking how good models understand and discuss important questions in Indian languages across cultures.
Why Indqa?
Opelai says that about 80 percent of people around the world do not speak English as their first language. However, most benchmarks that measure non-English skills are still limited and often rely on one or more translation formats.
Benchmarks like MMMLU and MGSM are now close to full at the high end, where models are strong near the same scores. This makes it difficult to see meaningful progress and does not check that the models understand the local context, history and everyday life.
India is Opelai's first entry point for a new regional focus on focused benches. India has nearly 1 billion non-native speakers, 22 official languages with at least 7 spoken by over 50 million people, and is the second largest market.
Dataset, Languages and Domains
INDQA examines knowledge and reasoning about Indian culture and daily life in Indian languages. The Benchmark poses 2,278 questions across 12 languages and 10 cultural domains, composed of 261 Domain Persinal Optors from across India.
The cultural domains are art and architecture, art and culture, daily life, food and nutrition, history, law and ethics, literature and entertainment, sports and recreation. The material is written in Bengali, English, Hindi, Hinglish, Kannada, Marathi, Odiya, Telugu, Gujarati, Malayalam, Punjabi and Tamil languages. Hinglish is included to show the code switching common in Indian conversations.
Each datapood consists of four components, a culture-based prompt in an Indian language, an English translation of the recovery, rubric methods of measurement and an ideal answer that includes the professional's expectations.
A rubric-based test pipeline
Indqa uses a rubric-based rating process instead of direct match accuracy. For each question, experts in the field describe several methods that define which strong answer to include or avoid and assign a weight to each method.
A grade based assessment evaluates the candidate's response to these procedures and grades are the most satisfactory. The final score is the sum of the satisfactory process weights divided by the total score. This behavior as including the success of the short answer, supports partial credit and captures the nuance and precision of culture, not just a surface token.
The process of building and sorting is opposed to that
Opelai defines a pipeline of four constructions:
First, they partnered with organizations in India to employ 10 experts. These professionals are native speakers of the target language and English and have deep subject problems. They write hard, show that they survive and promote the context of the region, such as literature, history of food, law or media.
Second, they apply a conflicting filter. All draft questions were tested with the most robust OpenAi models during creation, GPT-4O, Vulai O3, GPT-4.5 and, partially introduced after the public launch, GPT-5. Only questions where most of these types failed to produce acceptable responses were retained. This keeps the Header Hon
Third, experts provide detailed information on how to calculate each question, similar to an assessment rubric. These procedures are reused every time another model is tested in Indu.
Fourth, experts write the correct answers and English translations and do peer review and revision until they sign the quality.
Measuring progress in Indian languages
Opena is using Induct Indqa to test previous models and chart progress over the last few years in Indian languages. They report that the performance of the model has improved significantly in Induct while it is still out of the main development room. Results are divided by language and domain and include comparisons of GPT-5 Higher Thinking with other social programs.
Key acquisition
- INDQA is an exchmark of a custom-made index: Indqa examines how good models understand and reason about important questions in Indian languages, in all special cultural domains, rather than being able to assess only the translation or accuracy of multiple tests or accuracy.
- The dataset is well structured and large: The bench consists of 2,278 questions across 12 languages and 10 cultural domains, developed in collaboration with 261 domain experts from across India, covering areas of architecture, food, history and religion.
- Rubric testing is based on a rubric, not an exact match: Each dataPOINTGOINGS native language fast, English translation, detailed grading rubric and model model is written in an expert-based system, which gives the ability to get the money that it is.
- Queries are filtered against OpenAi's powerful models: Organized questions were filtered by the performance of GPT 4O, OpenAI O3, GPT 4.5 and GPT 5, and keep only those things where most of these types have failed, saving Headoom with future models of Indo.
INDQA is a timely initiative because it promotes a real gap, many different benchmarks in many languages over the Index in English content and translation style works while India has the highest languages. INDQA brings an expert, rubric assessment based on important questions in Indian cultural contexts, and is used to filter against those with GPT 4o, Open 4,5, GPT 4.5 and GPT 5 to save the Headoom of Frontier models. This launch makes Induc North Star an experiment to expose Indian languages to modern AI systems.
Michal Sutter is a data scientist with a Master of Science in Data Science from the University of PADOVA. With a strong foundation in statistical analysis, machine learning, and data engineering, Mikhali excels at turning complex data into actionable findings.
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