37 citations · 60 across the 7 of their papers we have counts for
5 papers · 1 filter
Critical Learning Periods: Leveraging Early Training Dynamics for Efficient Data Pruning
Everlyn Asiko Chimoto, Jay Gala, Orevaoghene Ahia +3
Neural Machine Translation models are extremely data and compute-hungry. However, not all data points contribute equally to model training and generalization. Data pruning to remov…
CVQA: Culturally-diverse Multilingual Visual Question Answering Benchmark
David Romero, Chenyang Lyu, Haryo Akbarianto Wibowo +73
Visual Question Answering (VQA) is an important task in multimodal AI, and it is often used to test the ability of vision-language models to understand and reason on knowledge pres…
On the low-shot transferability of [V]-Mamba
Diganta Misra, Jay Gala, Antonio Orvieto
The strength of modern large-scale neural networks lies in their ability to efficiently adapt to new tasks with few examples. Although extensive research has investigated the trans…
Airavata: Introducing Hindi Instruction-tuned LLM
Jay Gala, Thanmay Jayakumar, Jaavid Aktar Husain +8
We announce the initial release of "Airavata," an instruction-tuned LLM for Hindi. Airavata was created by fine-tuning OpenHathi with diverse, instruction-tuning Hindi datasets to…
An Empirical Study of In-context Learning in LLMs for Machine Translation
Pranjal A. Chitale, Jay Gala, Raj Dabre
Recent interest has surged in employing Large Language Models (LLMs) for machine translation (MT) via in-context learning (ICL) (Vilar et al., 2023). Most prior studies primarily f…