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 1576–1600 of 14297 papers

TitleStatusHype
Deep Triplet Hashing Network for Case-based Medical Image RetrievalCode1
DeepTrust: A Reliable Financial Knowledge Retrieval Framework For Explaining Extreme Pricing AnomaliesCode1
Learning Super-Features for Image RetrievalCode1
From Association to Generation: Text-only Captioning by Unsupervised Cross-modal MappingCode1
Defending Against Social Engineering Attacks in the Age of LLMsCode1
Democratizing Large Language Models via Personalized Parameter-Efficient Fine-tuningCode1
Delaying Interaction Layers in Transformer-based Encoders for Efficient Open Domain Question AnsweringCode1
Efficient k-NN Search with Cross-Encoders using Adaptive Multi-Round CUR DecompositionCode1
CBR-RAG: Case-Based Reasoning for Retrieval Augmented Generation in LLMs for Legal Question AnsweringCode1
A multi-task semi-supervised framework for Text2Graph & Graph2TextCode1
Learning To Generate Piano Music With Sustain PedalsCode1
Dense-Captioning Events in VideosCode1
From Artificially Real to Real: Leveraging Pseudo Data from Large Language Models for Low-Resource Molecule DiscoveryCode1
Dense Hierarchical Retrieval for Open-Domain Question AnsweringCode1
Auto-Encoding Twin-Bottleneck HashingCode1
ChatGPT-powered Conversational Drug Editing Using Retrieval and Domain FeedbackCode1
Dense Relational Image Captioning via Multi-task Triple-Stream NetworksCode1
DenserNet: Weakly Supervised Visual Localization Using Multi-scale Feature AggregationCode1
AutoKG: Efficient Automated Knowledge Graph Generation for Language ModelsCode1
Densifying Sparse Representations for Passage Retrieval by Representational SlicingCode1
Dense X Retrieval: What Retrieval Granularity Should We Use?Code1
Learning to SampleCode1
Learning to Tokenize for Generative RetrievalCode1
A Reproducible Extraction of Training Images from Diffusion ModelsCode1
CaseLink: Inductive Graph Learning for Legal Case RetrievalCode1
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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