3 papers
cs.CL2025
MMTEB: Massive Multilingual Text Embedding Benchmark
Kenneth Enevoldsen, Isaac Chung, Imene Kerboua +83
Text embeddings are typically evaluated on a limited set of tasks, which are constrained by language, domain, and task diversity. To address these limitations and provide a more co…
cs.CL2025
Late Chunking: Contextual Chunk Embeddings Using Long-Context Embedding Models
Michael Günther, Isabelle Mohr, Daniel James Williams +2
Many use cases require retrieving smaller portions of text, and dense vector-based retrieval systems often perform better with shorter text segments, as the semantics are less like…
cs.CL2025
jina-clip-v2: Multilingual Multimodal Embeddings for Text and Images
Andreas Koukounas, Georgios Mastrapas, Sedigheh Eslami +7
Contrastive Language-Image Pretraining (CLIP) has been widely used for crossmodal information retrieval and multimodal understanding tasks. However, CLIP models are mainly optimize…