2 papers
cs.CL2026
DEPART: DEcomposing PARiTy across Multilingual LLMs
Manan Uppadhyay, Prashant Kodali, Pranjal Chitale +3
Multilingual Large Language Models (mLLMs) leaderboards report per-language accuracy but rarely explain why disparities emerge, leaving systemic biases unattributed and offering pr…
cs.CL2026
UPDESH: Synthesizing Grounded Instruction Tuning Data for 13 Indic Languages
Pranjal A. Chitale, Varun Gumma, Sanchit Ahuja +4
Developing culturally grounded multilingual AI systems remains challenging, particularly for low-resource languages. While synthetic data offers promise, its effectiveness in multi…