14 citations · 49 across the 22 of their papers we have counts for
5 papers · 1 filter
MILU: A Multi-task Indic Language Understanding Benchmark
Sshubam Verma, Mohammed Safi Ur Rahman Khan, Vishwajeet Kumar +2
Evaluating Large Language Models (LLMs) in low-resource and linguistically diverse languages remains a significant challenge in NLP, particularly for languages using non-Latin scri…
Benchmarking and Building Zero-Shot Hindi Retrieval Model with Hindi-BEIR and NLLB-E5
Arkadeep Acharya, Rudra Murthy, Vishwajeet Kumar +1
Given the large number of Hindi speakers worldwide, there is a pressing need for robust and efficient information retrieval systems for Hindi. Despite ongoing research, comprehensi…
Mistral-SPLADE: LLMs for better Learned Sparse Retrieval
Meet Doshi, Vishwajeet Kumar, Rudra Murthy +2
Learned Sparse Retrievers (LSR) have evolved into an effective retrieval strategy that can bridge the gap between traditional keyword-based sparse retrievers and embedding-based de…
Hindi-BEIR : A Large Scale Retrieval Benchmark in Hindi
Arkadeep Acharya, Rudra Murthy, Vishwajeet Kumar +1
Given the large number of Hindi speakers worldwide, there is a pressing need for robust and efficient information retrieval systems for Hindi. Despite ongoing research, there is a…
INDIC QA BENCHMARK: A Multilingual Benchmark to Evaluate Question Answering capability of LLMs for Indic Languages
Abhishek Kumar Singh, Vishwajeet kumar, Rudra Murthy +3
Large Language Models (LLMs) perform well on unseen tasks in English, but their abilities in non English languages are less explored due to limited benchmarks and training data. To…