4 papers
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…
EfficientXLang: Towards Improving Token Efficiency Through Cross-Lingual Reasoning
Sanchit Ahuja, Praneetha Vaddamanu, Barun Patra
Despite recent advances in Language Reasoning Models (LRMs), most research focuses solely on English, even though many models are pretrained on multilingual data. In this work, we…
Contamination Report for Multilingual Benchmarks
Sanchit Ahuja, Varun Gumma, Sunayana Sitaram
Benchmark contamination refers to the presence of test datasets in Large Language Model (LLM) pre-training or post-training data. Contamination can lead to inflated scores on bench…
Scaling Laws for Multilingual Language Models
Yifei He, Alon Benhaim, Barun Patra +6
We propose a novel scaling law for general-purpose decoder-only language models (LMs) trained on multilingual data, tackling the problem of balancing languages during multilingual…