3 citations · 9 across the 7 of their papers we have counts for
5 papers · 1 filter
Beyond Semantic Similarity: Performance and Costs of Agentic Retrieval for Complex Tasks
Reza Esfandiarpoor, Radek Osmulski, Yauhen Babakhin +8
Modern information systems, including many agentic workflows, use dense retrieval to explore large amounts of unstructured data. However, dense retrieval relies on surface-level se…
Nemotron ColEmbed V2: Top-Performing Late Interaction Embedding Models for Visual Document Retrieval
Gabriel de Souza P. Moreira, Ronay Ak, Mengyao Xu +9
Retrieval-Augmented Generation (RAG) systems have been popular for generative applications, powering language models by injecting external knowledge. Companies have been trying to…
MIRACL-VISION: A Large, multilingual, visual document retrieval benchmark
Radek Osmulski, Gabriel de Souza P. Moreira, Ronay Ak +3
Document retrieval is an important task for search and Retrieval-Augmented Generation (RAG) applications. Large Language Models (LLMs) have contributed to improving the accuracy of…
Enhancing Q&A Text Retrieval with Ranking Models: Benchmarking, fine-tuning and deploying Rerankers for RAG
Gabriel de Souza P. Moreira, Ronay Ak, Benedikt Schifferer +3
Ranking models play a crucial role in enhancing overall accuracy of text retrieval systems. These multi-stage systems typically utilize either dense embedding models or sparse lexi…
NV-Retriever: Improving text embedding models with effective hard-negative mining
Gabriel de Souza P. Moreira, Radek Osmulski, Mengyao Xu +3
Text embedding models have been popular for information retrieval applications such as semantic search and Question-Answering systems based on Retrieval-Augmented Generation (RAG).…