The role of synthetic data in Multilingual, Multi-cultural AI systems:
  Lessons from Indic Languages
Authors: name: Pranjal A. Chitale, name: Varun Gumma, name: Sanchit Ahuja, name: Prashant Kodali, name: Manan Uppadhyay, name: Deepthi Sudharsan, name: Sunayana Sitaram
Organization: Microsoft

Developing AI systems that operate effectively across languages while
remaining culturally grounded is a long-standing challenge, particularly in
low-resource settings. Synthetic data provides a promising avenue, yet its
effectiveness in multilingual and multicultural contexts remains underexplored.
We investigate the creation and impact of synthetic, culturally contextualized
datasets for Indian languages through a bottom-up generation strategy that
prompts large open-source LLMs (>= 235B parameters) to ground data generation
in language-specific Wikipedia content. This approach complements the dominant
top-down paradigm of translating synthetic datasets from high-resource
languages such as English. We introduce Updesh, a high-quality large-scale
synthetic instruction-following dataset comprising 9.5M data points across 13
Indian languages, encompassing diverse reasoning and generative tasks with an
emphasis on long-context, multi-turn capabilities, and alignment with Indian
cultural contexts. A comprehensive evaluation incorporating both automated
metrics and human annotation across 10k assessments indicates that generated
data is high quality; though, human evaluation highlights areas for further
improvement. Additionally, we perform downstream evaluations by fine-tuning
models on our dataset and assessing the performance across 15 diverse
multilingual datasets. Models trained on Updesh consistently achieve
significant gains on generative tasks and remain competitive on multiple-choice
style NLU tasks. Notably, relative improvements are most pronounced in low and
medium-resource languages, narrowing their gap with high-resource languages.
These findings provide empirical evidence that effective multilingual AI
requires multi-faceted data curation and generation strategies that incorporate
context-aware, culturally grounded methodologies.