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 7601–7625 of 14297 papers

TitleStatusHype
A Method of Passage-Based Document Retrieval in Question Answering System—0
Learning Universal User Representations Leveraging Cross-domain User Intent at Snapchat—0
Adaptive Graph via Multiple Kernel Learning for Nonnegative Matrix Factorization—0
Learning User Representations for Open Vocabulary Image Hashtag Prediction—0
Prompting GPT-3.5 for Text-to-SQL with De-semanticization and Skeleton Retrieval—0
Holmes: Automated Fact Check with Large Language Models—0
Holistic Multi-modal Memory Network for Movie Question Answering—0
Learning Visual Hierarchies with Hyperbolic Embeddings—0
Attribute-Guided Network for Cross-Modal Zero-Shot Hashing—0
HoH: A Dynamic Benchmark for Evaluating the Impact of Outdated Information on Retrieval-Augmented Generation—0
Learning Visual N-Grams from Web Data—0
Learning Visual-Semantic Embeddings for Reporting Abnormal Findings on Chest X-rays—0
Correlations strike back (again): the case of associative memory retrieval—0
Learning When to Retrieve, What to Rewrite, and How to Respond in Conversational QA—0
Correlation Hashing Network for Efficient Cross-Modal Retrieval—0
A Method for Multi-Hop Question Answering on Persian Knowledge Graph—0
HiVLP: Hierarchical Vision-Language Pre-Training for Fast Image-Text Retrieval—0
Learning with Label Noise for Image Retrieval by Selecting Interactions—0
Learning with Multi-modal Gradient Attention for Explainable Composed Image Retrieval—0
Learning with Noisy Correspondence—0
HiVLP: Hierarchical Interactive Video-Language Pre-Training—0
Learning with Noisy Triplet Correspondence for Composed Image Retrieval—0
HiVeGen -- Hierarchical LLM-based Verilog Generation for Scalable Chip Design—0
Learning without the Phase: Regularized PhaseMax Achieves Optimal Sample Complexity—0
Correlation between Similarity Measures for Inter-Language Linked Wikipedia Articles—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