3 citations · 3 across the 3 of their papers we have counts for
4 papers · 1 filter
MMTEB: Massive Multilingual Text Embedding Benchmark
Kenneth Enevoldsen, Isaac Chung, Imene Kerboua +83
Text embeddings are typically evaluated on a limited set of tasks, which are constrained by language, domain, and task diversity. To address these limitations and provide a more co…
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…
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…