7 papers
ViDoRe V3: A Comprehensive Evaluation of Retrieval Augmented Generation in Complex Real-World Scenarios
António Loison, Quentin Macé, Antoine Edy +7
Retrieval-Augmented Generation (RAG) pipelines must address challenges beyond simple single-document retrieval, such as interpreting visual elements (tables, charts, images), synth…
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…
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…