The role of synthetic data in Multilingual, Multi-cultural AI systems:
  Lessons from Indic Languages
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.
68d6a3310177a6054b013678
Pranjal A. Chitale
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2025-09-26T15:36:00.535Z
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Pranjal A. Chitale
user
pranjalchitale
68d6a3310177a6054b013679
Varun Gumma
68d6a3310177a6054b01367a
Sanchit Ahuja
68d6a3310177a6054b01367b
Prashant Kodali
68d6a3310177a6054b01367c
Manan Uppadhyay
68d6a3310177a6054b01367d
Deepthi Sudharsan
68d6a3310177a6054b01367e
Sunayana Sitaram
The role of synthetic data in Multilingual, Multi-cultural AI systems:
  Lessons from Indic Languages
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.
Synthetic, culturally contextualized datasets for Indian languages improve multilingual AI performance, especially in low and medium-resource languages, through a bottom-up generation strategy using large open-source LLMs.
LLMs large-scale synthetic instruction-following dataset Updesh long-context multi-turn capabilities cultural contexts automated metrics human annotation fine-tuning generative tasks multiple-choice style NLU tasks multilingual datasets low-resource languages medium-resource languages high-resource languages