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 4026–4050 of 14297 papers

TitleStatusHype
Conflicts, Villains, Resolutions: Towards models of Narrative Media FramingCode0
Inexact Block Coordinate Descent Algorithms for Nonsmooth Nonconvex OptimizationCode0
InDiReCT: Language-Guided Zero-Shot Deep Metric Learning for ImagesCode0
Does Structure Matter? Leveraging Data-to-Text Generation for Answering Complex Information NeedsCode0
Does the Performance of Text-to-Image Retrieval Models Generalize Beyond Captions-as-a-Query?Code0
A Survey of Generative Information RetrievalCode0
IndicIRSuite: Multilingual Dataset and Neural Information Models for Indian LanguagesCode0
Indoor Visual Positioning Aided by CNN-Based Image Retrieval: Training-Free, 3D Modeling-FreeCode0
Approximate Nearest Neighbour Search on Privacy-aware Encoding of User Locations to Identify Susceptible Infections in Simulated EpidemicsCode0
Unipa-GPT: Large Language Models for university-oriented QA in ItalianCode0
Do Lessons from Metric Learning Generalize to Image-Caption Retrieval?Code0
Result Diversification in Search and Recommendation: A SurveyCode0
Aligning Hyperbolic Representations: an Optimal Transport-based approachCode0
Incremental Embedding Learning via Zero-Shot TranslationCode0
Incremental Multiview Point Cloud RegistrationCode0
Incorporating Legal Structure in Retrieval-Augmented Generation: A Case Study on Copyright Fair UseCode0
Concurrent Brainstorming & Hypothesis Satisfying: An Iterative Framework for Enhanced Retrieval-Augmented Generation (R2CBR3H-SR)Code0
CoNCRA: A Convolutional Neural Network Code Retrieval ApproachCode0
Improving Video Corpus Moment Retrieval with Partial Relevance EnhancementCode0
Improving zero-shot learning by mitigating the hubness problemCode0
Improving the robustness of ImageNet classifiers using elements of human visual cognitionCode0
A Surprisingly Simple yet Effective Multi-Query Rewriting Method for Conversational Passage RetrievalCode0
Improving Toponym Resolution with Better Candidate Generation, Transformer-based Reranking, and Two-Stage ResolutionCode0
Concentration Inequalities for Two-Sample Rank Processes with Application to Bipartite RankingCode0
Improving the Robustness of Dense Retrievers Against Typos via Multi-Positive Contrastive LearningCode0
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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