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 4226–4250 of 14297 papers

TitleStatusHype
Evaluating Temporal Persistence Using Replicability Measures—0
Evaluating the reliability of acoustic speech embeddings—0
Cross-Modality Personalization for Retrieval—0
Enhancing Low-Resource Minority Language Translation with LLMs and Retrieval-Augmented Generation for Cultural Nuances—0
Cross-Modality Binary Code Learning via Fusion Similarity Hashing—0
Autoencoding the Retrieval Relevance of Medical Images—0
Adaptive Semantic-Visual Tree for Hierarchical Embeddings—0
Evaluating Search System Explainability with Psychometrics and Crowdsourcing—0
Cross-modal Image Retrieval with Deep Mutual Information Maximization—0
Cross-Modal Hamming Hashing—0
AutoComPose: Automatic Generation of Pose Transition Descriptions for Composed Pose Retrieval Using Multimodal LLMs—0
A Multi-View Embedding Space for Modeling Internet Images, Tags, and their Semantics—0
Evaluating Self-Generated Documents for Enhancing Retrieval-Augmented Generation with Large Language Models—0
Cross-Modal Food Retrieval: Learning a Joint Embedding of Food Images and Recipes with Semantic Consistency and Attention Mechanism—0
Author-topic profiles for academic search—0
Cross-Modal Discrete Representation Learning—0
Evaluating Retrieval for Multi-domain Scientific Publications—0
Cross-Modal Denoising: A Novel Training Paradigm for Enhancing Speech-Image Retrieval—0
Cross-Modal Deep Variational Hashing—0
Authorship Verification - An Approach based on Random Forest—0
Cross-modal Deep Metric Learning with Multi-task Regularization—0
Cross-Modal Coordination Across a Diverse Set of Input Modalities—0
A Multi-task Learning Framework for Product Ranking with BERT—0
Evaluating Robustness of Generative Search Engine on Adversarial Factual Questions—0
Evaluating Sentence-Level Relevance Feedback for High-Recall Information 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