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 7451–7475 of 14297 papers

TitleStatusHype
LayoutBERT: Masked Language Layout Model for Object Insertion—0
Joint Multisided Exposure Fairness for RecommendationCode0
Leaner and Faster: Two-Stage Model Compression for Lightweight Text-Image RetrievalCode1
CLIP-Art: Contrastive Pre-training for Fine-Grained Art ClassificationCode2
Privacy-Preserving Model Upgrades with Bidirectional Compatible Training in Image RetrievalCode1
Reducing Predictive Feature Suppression in Resource-Constrained Contrastive Image-Caption RetrievalCode0
Semantic Information Recovery in Wireless NetworksCode1
Curriculum Learning for Dense Retrieval DistillationCode1
HybriDialogue: An Information-Seeking Dialogue Dataset Grounded on Tabular and Textual Data—0
Unaligned Supervision For Automatic Music Transcription in The WildCode1
Generative Multi-hop RetrievalCode1
Relevance-based Margin for Contrastively-trained Video Retrieval ModelsCode0
A Thorough Examination on Zero-shot Dense Retrieval—0
Cross-Camera Trajectories Help Person Retrieval in a Camera NetworkCode0
MILES: Visual BERT Pre-training with Injected Language Semantics for Video-text RetrievalCode1
When do Contrastive Word Alignments Improve Many-to-many Neural Machine Translation?—0
PLOD: An Abbreviation Detection Dataset for Scientific DocumentsCode1
Leveraging Unlabeled Data for Sketch-based Understanding—0
Efficient Machine Translation Domain AdaptationCode0
Offline Retrieval Evaluation Without Evaluation MetricsCode0
SceneTrilogy: On Human Scene-Sketch and its Complementarity with Photo and Text—0
Information Retrieval in Friction Stir Welding of Aluminum Alloys by using Natural Language Processing based Algorithms—0
Evaluating Interpolation and Extrapolation Performance of Neural Retrieval ModelsCode1
C3: Continued Pretraining with Contrastive Weak Supervision for Cross Language Ad-Hoc Retrieval—0
Incorporating Explicit Knowledge in Pre-trained Language Models for Passage Re-ranking—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