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 6326–6350 of 14297 papers

TitleStatusHype
Immediate, Scalable Object Category Detection—0
Clustering Algorithms and RAG Enhancing Semi-Supervised Text Classification with Large LLMs—0
Impact of detecting clinical trial elements in exploration of COVID-19 literature—0
Impact of Response Latency on User Behaviour in Mobile Web Search—0
Impact of Semantic Granularity on Geographic Information Search Support—0
Artpedia—0
A Multi-Task Architecture on Relevance-based Neural Query Translation—0
Implementation Notes for the Soft Cosine Measure—0
Implementation of an efficient Fuzzy Logic based Information Retrieval System—0
A Hybrid Approach to Generation of Missing Abstracts in Biomedical Literature—0
Cluster-Based Information Retrieval by using (K-means)- Hierarchical Parallel Genetic Algorithms Approach—0
Cross-modal Deep Metric Learning with Multi-task Regularization—0
Implicit Negative Feedback in Clinical Information Retrieval—0
Implicit Regularization in Nonconvex Statistical Estimation: Gradient Descent Converges Linearly for Phase Retrieval, Matrix Completion, and Blind Deconvolution—0
Implicit Regularization in Nonconvex Statistical Estimation: Gradient Descent Converges Linearly for Phase Retrieval and Matrix Completion—0
Implicit semantic-based personalized micro-videos recommendation—0
Cross-Modal Denoising: A Novel Training Paradigm for Enhancing Speech-Image Retrieval—0
Artificial Musical Intelligence: A Survey—0
Cluster-Aware Similarity Diffusion for Instance Retrieval—0
Improv Chat: Second Response Generation for Chatbot—0
Improved Answer Selection with Pre-Trained Word Embeddings—0
Improved Deep Classwise Hashing With Centers Similarity Learning for Image Retrieval—0
Improved Deep Metric Learning with Multi-class N-pair Loss Objective—0
A Hybrid Approach for Improved Content-based Image Retrieval using Segmentation—0
Interferences in match kernels—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