15 citations · 17 across the 6 of their papers we have counts for
7 papers · 1 filter
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…
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…
MASCA: LLM based-Multi Agents System for Credit Assessment
Gautam Jajoo, Atharva Pandey, Pranjal A Chitale +1
Recent advancements in financial problem-solving have leveraged LLMs and agent-based systems, with a primary focus on trading and financial modeling. However, credit assessment rem…
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…
Towards Inducing Long-Context Abilities in Multilingual Neural Machine Translation Models
Varun Gumma, Pranjal A. Chitale, Kalika Bali
Neural Machine Translation (NMT) models have traditionally used Sinusoidal Positional Embeddings (PEs), which often struggle to capture long-range dependencies and are inefficient…
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…