6 papers
Principles of Concept Representation in Sentence Encoders
Isabelle Mohr, John Dujany, Jonathan Souquet +1
What makes a sentence encoder produce good concept representations? We approach this through the lens of representational compositionality: an encoder supports a concept family onl…
Reflective Reasoning for SQL Generation
Isabelle Mohr, Joao Gandarela, John Dujany +1
Robust text-to-SQL over complex, real-world databases remains brittle even with modern LLMs: iterative refinement often introduces syntactic and semantic drift, corrections tend to…
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…
jina-embeddings-v4: Universal Embeddings for Multimodal Multilingual Retrieval
Michael Günther, Saba Sturua, Mohammad Kalim Akram +8
We introduce jina-embeddings-v4, a 3.8 billion parameter multimodal embedding model that unifies text and image representations through a novel architecture supporting both single-…
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…
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…