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 8426–8450 of 14297 papers

TitleStatusHype
Asymmetrically Weighted CCA And Hierarchical Kernel Sentence Embedding For Image & Text Retrieval—0
AdapterDistillation: Non-Destructive Task Composition with Knowledge Distillation—0
GeoCapsNet: Aerial to Ground view Image Geo-localization using Capsule Network—0
GenTREC: The First Test Collection Generated by Large Language Models for Evaluating Information Retrieval Systems—0
ContextBLIP: Doubly Contextual Alignment for Contrastive Image Retrieval from Linguistically Complex Descriptions—0
GenQREnsemble: Zero-Shot LLM Ensemble Prompting for Generative Query Reformulation—0
Context-aware Tree-based Deep Model for Recommender Systems—0
A Survey on Video Moment Localization—0
Gen-IR @ SIGIR 2023: The First Workshop on Generative Information Retrieval—0
Genetic Algorithms for the Optimization of Diffusion Parameters in Content-Based Image Retrieval—0
Context-Aware Search and Retrieval Over Erasure Channels—0
A Survey on Retrieval of Mathematical Knowledge—0
Alleviating LLM-based Generative Retrieval Hallucination in Alipay Search—0
Retrieval from Mixed Sampling Frequency: Generic Identifiability in the Unit Root VAR—0
Generic Error Bounds for the Generalized Lasso with Sub-Exponential Data—0
Generic decoding of seen and imagined objects using hierarchical visual features—0
Context-aware query design combines knowledge and data for efficient reading and reasoning—0
A Survey on Retrieval-Augmented Text Generation for Large Language Models—0
Generative Zero-Shot Composed Image Retrieval—0
Generative Video Diffusion for Unseen Cross-Domain Video Moment Retrieval—0
Generative Transformers for Design Concept Generation—0
A Survey on Retrieval-Augmented Text Generation—0
All About VLAD—0
Generative Stock Question Answering—0
Generative Semantic Hashing Enhanced via Boltzmann Machines—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