Papers MTEB Benchmark
“MTEB Benchmark” 태그가 달린 논문 13편 · 필터 해제
GATE: General Arabic Text Embedding for Enhanced Semantic Textual Similarity with Matryoshka Representation Learning and Hybrid Loss Training
Semantic textual similarity (STS) is a critical task in natural language processing (NLP), enabling applications in retrieval, clustering, and understanding semantic relationships between texts. However, research in this…
MTEB BenchmarkNatural Language InferenceRepresentation LearningSemantic Similarity+3Optimization of embeddings storage for RAG systems using quantization and dimensionality reduction techniques
Retrieval-Augmented Generation enhances language models by retrieving relevant information from external knowledge bases, relying on high-dimensional vector embeddings typically stored in float32 precision. However, stor…
Dimensionality ReductionMTEB BenchmarkQuantizationRAG+1NeoBERT: A Next-Generation BERT
Recent innovations in architecture, pre-training, and fine-tuning have led to the remarkable in-context learning and reasoning abilities of large auto-regressive language models such as LLaMA and DeepSeek. In contrast, e…
In-Context LearningMTEB BenchmarkFaMTEB: Massive Text Embedding Benchmark in Persian Language
In this paper, we introduce a comprehensive benchmark for Persian (Farsi) text embeddings, built upon the Massive Text Embedding Benchmark (MTEB). Our benchmark includes 63 datasets spanning seven different tasks: classi…
ChatbotMTEB BenchmarkRerankingRetrieval+2KaLM-Embedding: Superior Training Data Brings A Stronger Embedding Model
As retrieval-augmented generation prevails in large language models, embedding models are becoming increasingly crucial. Despite the growing number of general embedding models, prior work often overlooks the critical rol…
MTEB BenchmarkRetrieval-augmented GenerationGenEOL: Harnessing the Generative Power of LLMs for Training-Free Sentence Embeddings
Training-free embedding methods directly leverage pretrained large language models (LLMs) to embed text, bypassing the costly and complex procedure of contrastive learning. Previous training-free embedding methods have m…
Contrastive LearningMTEB BenchmarkRerankingSentence+6Contextual Document Embeddings
Dense document embeddings are central to neural retrieval. The dominant paradigm is to train and construct embeddings by running encoders directly on individual documents. In this work, we argue that these embeddings, wh…
Contrastive LearningDocument EmbeddingGPUMTEB Benchmark+2jina-embeddings-v3: Multilingual Embeddings With Task LoRA
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 tasks, supporting context lengths of up to …
MTEB BenchmarkRepresentation LearningRetrievalText MatchingA Bi-metric Framework for Fast Similarity Search
We propose a new "bi-metric" framework for designing nearest neighbor data structures. Our framework assumes two dissimilarity functions: a ground-truth metric that is accurate but expensive to compute, and a proxy metri…
MTEB BenchmarkRe-RankingText RetrievalRecent advances in text embedding: A Comprehensive Review of Top-Performing Methods on the MTEB Benchmark
Text embedding methods have become increasingly popular in both industrial and academic fields due to their critical role in a variety of natural language processing tasks. The significance of universal text embeddings h…
DiversityMTEB BenchmarkRetrievalSynthetic Data GenerationJina Embeddings 2: 8192-Token General-Purpose Text Embeddings for Long Documents
Text embedding models have emerged as powerful tools for transforming sentences into fixed-sized feature vectors that encapsulate semantic information. While these models are essential for tasks like information retrieva…
Information RetrievalMTEB BenchmarkRe-RankingRetrievalC-Pack: Packed Resources For General Chinese Embeddings
We introduce C-Pack, a package of resources that significantly advance the field of general Chinese embeddings. C-Pack includes three critical resources. 1) C-MTEB is a comprehensive benchmark for Chinese text embeddings…
MTEB BenchmarkText Embeddings by Weakly-Supervised Contrastive Pre-training
This paper presents E5, a family of state-of-the-art text embeddings that transfer well to a wide range of tasks. The model is trained in a contrastive manner with weak supervision signals from our curated large-scale te…
MTEB BenchmarkOnly Connect Walls Dataset Task 1 (Grouping)Retrieval