SOTAVerified

Retrieval

A methodology that involves selecting relevant data or examples from a large dataset to support tasks like prediction, learning, or inference. It enhances models by providing context or additional information, often used in systems like retrieval-augmented generation or in-context learning.

Papers

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Showing 8276–8300 of 14297 papers

TitleStatusHype
Contrastive Document Representation Learning with Graph Attention Networks—0
Graph-based Multilingual Product Retrieval in E-commerce Search—0
Contrastive Cross-Modal Knowledge Sharing Pre-training for Vision-Language Representation Learning and Retrieval—0
A Telecom-Domain Online Customer Service Assistant Based on Question Answering with Word Embedding and Intent Classification—0
Graph-Based Cross-Domain Knowledge Distillation for Cross-Dataset Text-to-Image Person Retrieval—0
Graph-based Approximate NN Search: A Revisit—0
Graph-based Approaches and Functionalities in Retrieval-Augmented Generation: A Comprehensive Survey—0
Graph-augmented Learning to Rank for Querying Large-scale Knowledge Graph—0
ATEB: Evaluating and Improving Advanced NLP Tasks for Text Embedding Models—0
ALOHA: Empowering Multilingual Agent for University Orientation with Hierarchical Retrieval—0
Granularity-aware Adaptation for Image Retrieval over Multiple Tasks—0
Granite Embedding Models—0
Gram Regularization for Multi-view 3D Shape Retrieval—0
Grammatical-Error-Aware Incorrect Example Retrieval System for Learners of Japanese as a Second Language—0
Contrast & Compress: Learning Lightweight Embeddings for Short Trajectories—0
A Task-Oriented Dialogue Architecture via Transformer Neural Language Models and Symbolic Injection—0
GRAM: Generative Retrieval Augmented Matching of Data Schemas in the Context of Data Security—0
A logical representation of Arabic questions toward automatic passage extraction from the Web—0
Adapting Standard Retrieval Benchmarks to Evaluate Generated Answers—0
Grafit: Learning fine-grained image representations with coarse labels—0
Continuous Approach to Phase (Norm) Retrieval Frames—0
GRAF: Graph Retrieval Augmented by Facts for Romanian Legal Multi-Choice Question Answering—0
GradML: A Gradient-based Loss for Deep Metric Learning—0
Continuous Active Learning Using Pretrained Transformers—0
Gradient Descent with Random Initialization: Fast Global Convergence for Nonconvex Phase Retrieval—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1BM25SQueries per second183.53—Unverified
2ElasticsearchQueries per second21.8—Unverified
3BM25-PTQueries per second6.49—Unverified
4Rank-BM25Queries per second1.18—Unverified
#ModelMetricClaimedVerifiedStatus
1BM25SQueries per second20.88—Unverified
2ElasticsearchQueries per second7.11—Unverified
3Rank-BM25Queries per second0.04—Unverified
#ModelMetricClaimedVerifiedStatus
1BM25SQueries per second41.85—Unverified
2ElasticsearchQueries per second12.16—Unverified
3Rank-BM25Queries per second0.1—Unverified
#ModelMetricClaimedVerifiedStatus
1FLMRRecall@589.32—Unverified
2RA-VQARecall@582.84—Unverified
#ModelMetricClaimedVerifiedStatus
1PreFLMRRecall@562.1—Unverified
#ModelMetricClaimedVerifiedStatus
1CLIP-KIStext-to-video Mean Rank30—Unverified
#ModelMetricClaimedVerifiedStatus
1CLIP4OutfitRecall@57.59—Unverified
#ModelMetricClaimedVerifiedStatus
1MetaGen Blended RAGAccuracy (Top-1)82.1—Unverified
#ModelMetricClaimedVerifiedStatus
1MetaGen Blended RAGAccuracy (Top-1)82.1—Unverified
#ModelMetricClaimedVerifiedStatus
1COLTCOMP@84.55—Unverified
#ModelMetricClaimedVerifiedStatus
1hello0L1,121,222—Unverified