14 citations · 32 across the 10 of their papers we have counts for
11 papers
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…
HierarchicalKV: A GPU Hash Table with Cache Semantics for Continuous Online Embedding Storage
Haidong Rong, Jiashu Yao, Matthias Langer +12
Traditional GPU hash tables preserve every inserted key -- a dictionary assumption that wastes scarce High Bandwidth Memory (HBM) when embedding tables routinely exceed single-GPU…
Llama-Embed-Nemotron-8B: A Universal Text Embedding Model for Multilingual and Cross-Lingual Tasks
Yauhen Babakhin, Radek Osmulski, Ronay Ak +5
We introduce llama-embed-nemotron-8b, an open-weights text embedding model that achieves state-of-the-art performance on the Multilingual Massive Text Embedding Benchmark (MMTEB) l…
Omni-Embed-Nemotron: A Unified Multimodal Retrieval Model for Text, Image, Audio, and Video
Mengyao Xu, Wenfei Zhou, Yauhen Babakhin +6
We present Omni-Embed-Nemotron, a unified multimodal retrieval embedding model developed to handle the increasing complexity of real-world information needs. While Retrieval-Augmen…
Llama Nemoretriever Colembed: Top-Performing Text-Image Retrieval Model
Mengyao Xu, Gabriel Moreira, Ronay Ak +5
Motivated by the growing demand for retrieval systems that operate across modalities, we introduce llama-nemoretriever-colembed, a unified text-image retrieval model that delivers…
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).…
LlamaRec: Two-Stage Recommendation using Large Language Models for Ranking
Zhenrui Yue, Sara Rabhi, Gabriel de Souza Pereira Moreira +2
Recently, large language models (LLMs) have exhibited significant progress in language understanding and generation. By leveraging textual features, customized LLMs are also applie…
Transformers with multi-modal features and post-fusion context for e-commerce session-based recommendation
Gabriel de Souza P. Moreira, Sara Rabhi, Ronay Ak +2
Session-based recommendation is an important task for e-commerce services, where a large number of users browse anonymously or may have very distinct interests for different sessio…