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 7551–7575 of 14297 papers

TitleStatusHype
Hotel Recognition via Latent Image Embedding—0
Learning to Rank by Optimizing NDCG Measure—0
HORUS: Multimodal Large Language Models Framework for Video Retrieval at VBS 2025—0
Learning to Rank for Active Learning: A Listwise Approach—0
Horizon Scans can be accelerated using novel information retrieval and artificial intelligence tools—0
Learning to Rank for Multiple Retrieval-Augmented Models through Iterative Utility Maximization—0
CosmoCLIP: Generalizing Large Vision-Language Models for Astronomical Imaging—0
Learning to Rank in the Age of Muppets: Effectiveness–Efficiency Tradeoffs in Multi-Stage Ranking—0
HopRetriever: Retrieve Hops over Wikipedia to Answer Complex Questions—0
Learning to Rank Semantic Coherence for Topic Segmentation—0
HopRAG: Multi-Hop Reasoning for Logic-Aware Retrieval-Augmented Generation—0
Learning to Rank with Small Set of Ground Truth Data—0
CoSe-Co: Text Conditioned Generative CommonSense Contextualizer—0
Hopfield model with planted patterns: a teacher-student self-supervised learning model—0
Learning to Recognise Words using Visually Grounded Speech—0
Learning to Reformulate the Queries on the WEB—0
CO-Search: COVID-19 Information Retrieval with Semantic Search, Question Answering, and Abstractive Summarization—0
Attribute-Modulated Generative Meta Learning for Zero-Shot Classification—0
A Method to Disambiguate a Word by Using Restricted Boltzmann Machine—0
Homomorphic Sensing of Subspace Arrangements—0
Co-Saliency Detection via Looking Deep and Wide—0
Learning to Retrieve for Job Matching—0
Homography augumented momentum constrastive learning for SAR image retrieval—0
Learning to Retrieve Iteratively for In-Context Learning—0
Homa at SemEval-2025 Task 5: Aligning Librarian Records with OntoAligner for Subject Tagging—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