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 8351–8375 of 14297 papers

TitleStatusHype
AugmentedCode: Examining the Effects of Natural Language Resources in Code Retrieval ModelsCode1
Controllable Semantic Parsing via Retrieval AugmentationCode0
Noise-Augmented Privacy-Preserving Empirical Risk Minimization with Dual-purpose Regularizer and Privacy Budget Retrieval and Recycling—0
Video-Data Pipelines for Machine Learning ApplicationsCode0
SPoT: Better Frozen Model Adaptation through Soft Prompt Transfer—0
Cascaded Fast and Slow Models for Efficient Semantic Code Search—0
DS-TOD: Efficient Domain Specialization for Task Oriented DialogCode0
DialFact: A Benchmark for Fact-Checking in DialogueCode1
PolyNet: Polynomial Neural Network for 3D Shape Recognition with PolyShape RepresentationCode1
Few-Shot Bot: Prompt-Based Learning for Dialogue SystemsCode1
Tagged Documents Co-Clustering—0
Retrieval-guided Counterfactual Generation for QA—0
Spoken ObjectNet: A Bias-Controlled Spoken Caption DatasetCode0
RocketQAv2: A Joint Training Method for Dense Passage Retrieval and Passage Re-ranking—0
Cross-Lingual Open-Domain Question Answering with Answer Sentence Generation—0
Creating User Interface Mock-ups from High-Level Text Descriptions with Deep-Learning Models—0
Sentence-aware Contrastive Learning for Open-Domain Passage Retrieval—0
Zero-Shot Dense Retrieval with Momentum Adversarial Domain Invariant Representations—0
Coarse to Fine: Video Retrieval before Moment Localization—0
Exposing Query Identification for Search Transparency—0
MDERank: A Masked Document Embedding Rank Approach for Unsupervised Keyphrase ExtractionCode1
Improving Users' Mental Model with Attention-directed Counterfactual Edits—0
Color Counting for Fashion, Art, and Design—0
Exploring Dense Retrieval for Dialogue Response SelectionCode1
Salient Phrase Aware Dense Retrieval: Can a Dense Retriever Imitate a Sparse One?Code1
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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