1 citations · 3 across the 16 of their papers we have counts for
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CAMI: Cost-Aware Agent-Guided Multi-Indexing for Semantic Retrieval
Adnan Qidwai, Anand Eswaran, Sonam Mishra +2
RAG ingestion pipelines frequently augment search corpus index with semantic enrichment indices (e.g., synthetic queries or summaries generated from corpus chunks) that are subsequ…
Granite Embedding Multilingual R2 Models
Parul Awasthy, Aashka Trivedi, Yushu Yang +14
We introduce the multilingual Granite Embedding R2 models, a family of encoder-based embedding models for enterprise-scale dense retrieval across 200+ languages. Extending our Engl…
Influence Guided Sampling for Domain Adaptation of Text Retrievers
Meet Doshi, Vishwajeet Kumar, Yulong Li +1
General-purpose open-domain dense retrieval systems are usually trained with a large, eclectic mix of corpora and search tasks. How should these diverse corpora and tasks be sample…
Granite Embedding Models
Parul Awasthy, Aashka Trivedi, Yulong Li +19
We introduce the Granite Embedding models, a family of encoder-based embedding models designed for retrieval tasks, spanning dense-retrieval and sparse retrieval architectures, wit…
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…