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 10351–10375 of 14297 papers

TitleStatusHype
Distill-SynthKG: Distilling Knowledge Graph Synthesis Workflow for Improved Coverage and Efficiency—0
Distinguishing artefacts: evaluating the saturation point of convolutional neural networks—0
Distortion-Aware Phase Retrieval Receiver for High-Order QAM Transmission with Carrierless Intensity-Only Measurements—0
Distributed Data Vending on Blockchain—0
Distributed Deep Learning for Persistent Monitoring of agricultural Fields—0
Distributed Evaluations: Ending Neural Point Metrics—0
Distributed Evolution of Deep Autoencoders—0
On Distributed Non-convex Optimization: Projected Subgradient Method For Weakly Convex Problems in Networks—0
Distributed Representations for Biological Sequence Analysis—0
Distribution Aligned Feature Clustering for Zero-Shot Sketch-Based Image Retrieval—0
Distribution-Aligned Fine-Tuning for Efficient Neural Retrieval—0
Distributionally Robust Multi-Output Regression Ranking—0
Distributional Thesauri for Information Retrieval and vice versa—0
Distributional Vision-Language Alignment by Cauchy-Schwarz Divergence—0
Diverse legal case search—0
Diverse Multi-Answer Retrieval with Determinantal Point Processes—0
Diverse Yet Efficient Retrieval using Hash Functions—0
Diversifying Reply Suggestions using a Matching-Conditional Variational Autoencoder—0
Diversify-verify-adapt: Efficient and Robust Retrieval-Augmented Ambiguous Question Answering—0
Diversity driven Query Rewriting in Search Advertising—0
DivGraphPointer: A Graph Pointer Network for Extracting Diverse Keyphrases—0
Divide and Conquer: Text Semantic Matching with Disentangled Keywords and Intents—0
Divide and Conquer: Towards Better Embedding-based Retrieval for Recommender Systems From a Multi-task Perspective—0
Divide and Rule: Effective Pre-Training for Context-Aware Multi-Encoder Translation Models—0
Divide by Question, Conquer by Agent: SPLIT-RAG with Question-Driven Graph Partitioning—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