17 citations · 37 across the 13 of their papers we have counts for
13 papers
Write-Read Decoupling in Modern Large-Scale Search Engines: Architectures, Techniques, and Emerging Approaches
Xin Liang, Qing Yang, Wenru Qiu +4
Large-scale search engines face a fundamental tension: the index must be updated frequently to maintain freshness, yet updates create resource contention that inflates query latenc…
PixelPrune: Pixel-Level Adaptive Visual Token Reduction via Predictive Coding
Nan Wang, Zhiwei Jin, Chen Chen +1
Document understanding and GUI interaction are among the highest-value applications of Vision-Language Models (VLMs), yet they impose exceptionally heavy computational burden: fine…
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-…
ReaderLM-v2: Small Language Model for HTML to Markdown and JSON
Feng Wang, Zesheng Shi, Bo Wang +2
We present ReaderLM-v2, a compact 1.5 billion parameter language model designed for efficient web content extraction. Our model processes documents up to 512K tokens, transforming…
AATS: Retrieval-Based KV Cache Reduction via Windowed Rotary Position Embedding and Query-Aware Vector Quantization
Junhui He, Junna Xing, Nan Wang +6
Long context large language models (LLMs) pose significant challenges for efficient serving due to the large memory footprint and high access overhead of KV cache. Retrieval-based…
AIR-Bench: Automated Heterogeneous Information Retrieval Benchmark
Jianlyu Chen, Nan Wang, Chaofan Li +6
Evaluation plays a crucial role in the advancement of information retrieval (IR) models. However, current benchmarks, which are based on predefined domains and human-labeled data,…
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…
jina-embeddings-v3: Multilingual Embeddings With Task LoRA
Saba Sturua, Isabelle Mohr, Mohammad Kalim Akram +8
We introduce jina-embeddings-v3, a novel text embedding model with 570 million parameters, achieves state-of-the-art performance on multilingual data and long-context retrieval tas…
Jina-ColBERT-v2: A General-Purpose Multilingual Late Interaction Retriever
Rohan Jha, Bo Wang, Michael Günther +7
Multi-vector dense models, such as ColBERT, have proven highly effective in information retrieval. ColBERT's late interaction scoring approximates the joint query-document attentio…
Jina CLIP: Your CLIP Model Is Also Your Text Retriever
Andreas Koukounas, Georgios Mastrapas, Michael Günther +11
Contrastive Language-Image Pretraining (CLIP) is widely used to train models to align images and texts in a common embedding space by mapping them to fixed-sized vectors. These mod…
Leveraging Passage Embeddings for Efficient Listwise Reranking with Large Language Models
Qi Liu, Bo Wang, Nan Wang +1
Recent studies have demonstrated the effectiveness of using large language language models (LLMs) in passage ranking. The listwise approaches, such as RankGPT, have become new stat…
Multi-Task Contrastive Learning for 8192-Token Bilingual Text Embeddings
Isabelle Mohr, Markus Krimmel, Saba Sturua +16
We introduce a novel suite of state-of-the-art bilingual text embedding models that are designed to support English and another target language. These models are capable of process…
Jina Embeddings 2: 8192-Token General-Purpose Text Embeddings for Long Documents
Michael Günther, Jackmin Ong, Isabelle Mohr +10
Text embedding models have emerged as powerful tools for transforming sentences into fixed-sized feature vectors that encapsulate semantic information. While these models are essen…