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 10501–10525 of 14297 papers

TitleStatusHype
DWT-CompCNN: Deep Image Classification Network for High Throughput JPEG 2000 Compressed Documents—0
DynaGRAG | Exploring the Topology of Information for Advancing Language Understanding and Generation in Graph Retrieval-Augmented Generation—0
Dynamic 3-D measurement based on fringe-to-fringe transformation using deep learning—0
Dynamical Origin for Winner-Take-All Competition in A Biological Network of The Hippocampal Dentate Gyrus—0
Dynamic Boundary Time Warping for Sub-sequence Matching with Few Examples—0
Dynamic Contexts for Generating Suggestion Questions in RAG Based Conversational Systems—0
Dynamic Context Tuning for Retrieval-Augmented Generation: Enhancing Multi-Turn Planning and Tool Adaptation—0
Dynamic Contrastive Distillation for Image-Text Retrieval—0
Dynamic Difficulty Awareness Training for Continuous Emotion Prediction—0
Dynamic Entity Memory Network for Dialogue Relational Triplet Extraction—0
Dynamic Feature Generation Network for Answer Selection—0
NELLIE: A Neuro-Symbolic Inference Engine for Grounded, Compositional, and Explainable Reasoning—0
Dynamic gesture retrieval: searching videos by human pose sequence—0
Dynamic Gradient Reactivation for Backward Compatible Person Re-identification—0
Dynamic Information Retrieval: Theoretical Framework and Application—0
Dynamic Neuro-Symbolic Knowledge Graph Construction for Zero-shot Commonsense Question Answering—0
Dynamic Metric Learning from Pairwise Comparisons—0
Dynamic Model for Query-Document Expansion towards Improving Retrieval Relevance—0
Dynamic Multi-Agent Orchestration and Retrieval for Multi-Source Question-Answer Systems using Large Language Models—0
DynamicRetriever: A Pre-training Model-based IR System with Neither Sparse nor Dense Index—0
Dynamic Superblock Pruning for Fast Learned Sparse Retrieval—0
Dynamic Thresholding Mechanisms for IR-Based Filtering in Efficient Source Code Plagiarism Detection—0
Dynamic Token Selection for Aerial-Ground Person Re-Identification—0
Dynamic Uncertainty Learning with Noisy Correspondence for Text-Based Person Search—0
COPR -- Efficient, large-scale log storage and retrieval—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