4 papers
Contextual Embeddings Beyond the Gold Chunk
Sedigheh Eslami, Markus Krimmel, William Barber +7
Contextual embeddings represent chunks of a document with respect to the document's global context. These models are typically trained and evaluated under the assumption that a rel…
Q2D-Web: A Large-Scale Benchmark for Retrieval in Agentic RAG Systems
Maximilian Schall, Sedigheh Eslami, Markus Krimmel +4
Evaluating first-stage retrievers in large-scale production RAG requires a benchmark that pairs a large-scale corpus with a large set of agent-reformulated search queries based on…
WANDR: A Benchmark for Wide and Deep Research
Vitaliy Polshkov, Marcin Pitera, Jeremy Yang +7
WANDR (Wide ANd Deep Research) is a benchmark of 500 realistic, challenging data-collection tasks for research agents. Each task requires a system to discover a large set of entiti…
Diffusion-Pretrained Dense and Contextual Embeddings
Sedigheh Eslami, Maksim Gaiduk, Markus Krimmel +3
In this report, we introduce pplx-embed, a family of multilingual embedding models that employ multi-stage contrastive learning on a diffusion-pretrained language model backbone fo…