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 8601–8625 of 14297 papers

TitleStatusHype
Embedding-based Product Retrieval in Taobao Search—0
Embedding based on function approximation for large scale image search—0
Embedding Arithmetic of Multimodal Queries for Image Retrieval—0
A French Corpus for Semantic Similarity—0
A Comprehensive Evaluation of Large Language Models on Temporal Event Forecasting—0
Embedding And Clustering Your Data Can Improve Contrastive Pretraining—0
Approximate Message Passing with Spectral Initialization for Generalized Linear Models—0
Multivariate Business Process Representation Learning utilizing Gramian Angular Fields and Convolutional Neural Networks—0
Embark on DenseQuest: A System for Selecting the Best Dense Retriever for a Custom Collection—0
CaseEncoder: A Knowledge-enhanced Pre-trained Model for Legal Case Encoding—0
Multivariate Representation Learning for Information Retrieval—0
Emancipatory Information Retrieval—0
eLog analysis for accelerators: status and future outlook—0
Case-based Similar Image Retrieval for Weakly Annotated Large Histopathological Images of Malignant Lymphoma Using Deep Metric Learning—0
ELKI: A large open-source library for data analysis - ELKI Release 0.7.5 "Heidelberg"—0
ELIXR: Towards a general purpose X-ray artificial intelligence system through alignment of large language models and radiology vision encoders—0
Approximate Knowledge Graph Query Answering: From Ranking to Binary Classification—0
A Free Format Legal Question Answering System—0
Multi-Vector Attention Models for Deep Re-ranking—0
ELIP: Enhanced Visual-Language Foundation Models for Image Retrieval—0
Case-based Reasoning Augmented Large Language Model Framework for Decision Making in Realistic Safety-Critical Driving Scenarios—0
Eliminating Search Intent Bias in Learning to Rank—0
Approximate Cluster-Based Sparse Document Retrieval with Segmented Maximum Term Weights—0
3D Object Recognition By Corresponding and Quantizing Neural 3D Scene Representations—0
Eliciting In-context Retrieval and Reasoning for Long-context Large Language Models—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