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 4701–4725 of 14297 papers

TitleStatusHype
FedHAP: Federated Hashing with Global Prototypes for Cross-silo Retrieval—0
Asymmetric Transfer Hashing with Adaptive Bipartite Graph Learning—0
Contextualization of topics - browsing through terms, authors, journals and cluster allocations—0
All the attention you need: Global-local, spatial-channel attention for image retrieval—0
Contextualization of ASR with LLM using phonetic retrieval-based augmentation—0
Contextualization for the Organization of Text Documents Streams—0
Asymmetric Sparse Kernel Approximations for Large-scale Visual Search—0
Contextualised Browsing in a Digital Library's Living Lab—0
Contextual Fine-to-Coarse Distillation for Coarse-grained Response Selection in Open-Domain Conversations—0
Asymmetric Scalable Cross-modal Hashing—0
FeB4RAG: Evaluating Federated Search in the Context of Retrieval Augmented Generation—0
FecTek: Enhancing Term Weight in Lexicon-Based Retrieval with Feature Context and Term-level Knowledge—0
Federated Neural Graph Databases—0
Contextual Document Embeddings—0
Feature Selection for Better Spectral Characterization or: How I Learned to Start Worrying and Love Ensembles—0
Context Tuning for Retrieval Augmented Generation—0
Asymmetric Leaky Private Information Retrieval—0
Allowing for equal opportunities for artists in music recommendation—0
Features-over-the-Air: Contrastive Learning Enabled Cooperative Edge Inference—0
Context Sensitive Article Ranking with Citation Context Analysis—0
Adapting Dual-encoder Vision-language Models for Paraphrased Retrieval—0
Asymmetric Feature Maps with Application to Sketch Based Retrieval—0
A Bioinformatics Study for Recognition of Hub Genes and Pathways in Pancreatic Ductal Adenocarcinoma—0
Feature Selection and Model Comparison on Microsoft Learning-to-Rank Data Sets—0
Feature Super-Resolution: Make Machine See More Clearly—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