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 8476–8500 of 14297 papers

TitleStatusHype
Generative Product Recommendations for Implicit Superlative Queries—0
Content-based Video Indexing and Retrieval Using Corr-LDA—0
Generative or Contrastive? Phrase Reconstruction for Better Sentence Representation Learning—0
Content-Based Table Retrieval for Web Queries—0
A Survey on Non-rigid 3D Shape Analysis—0
Generative One-Class Models for Text-based Person Retrieval in Forensic Applications—0
Generative Negative Text Replay for Continual Vision-Language Pretraining—0
Generative Model for Zero-Shot Sketch-Based Image Retrieval—0
Content-based similar document image retrieval using fusion of CNN features—0
A Survey On Neural Word Embeddings—0
Natural Language Programming in Medicine: Administering Evidence Based Clinical Workflows with Autonomous Agents Powered by Generative Large Language Models—0
Generative Language Models with Retrieval Augmented Generation for Automated Short Answer Scoring—0
Generative Information Retrieval Evaluation—0
Generative Ghost: Investigating Ranking Bias Hidden in AI-Generated Videos—0
Content-based Representations of audio using Siamese neural networks—0
Multi-hop Question Answering—0
A Linguistic Study on Relevance Modeling in Information Retrieval—0
Adapt and Decompose: Efficient Generalization of Text-to-SQL via Domain Adapted Least-To-Most Prompting—0
Content-based Music Similarity with Triplet Networks—0
Generative Cross-Modal Retrieval: Memorizing Images in Multimodal Language Models for Retrieval and Beyond—0
Generative Attribute Controller With Conditional Filtered Generative Adversarial Networks—0
Content-Based Medical Image Retrieval with Opponent Class Adaptive Margin Loss—0
A Survey on Multi-Behavior Sequential Recommendation—0
Generative and Pseudo-Relevant Feedback for Sparse, Dense and Learned Sparse Retrieval—0
Generative AI for Software Metadata: Overview of the Information Retrieval in Software Engineering Track at FIRE 2023—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