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

Showing 43014325 of 14297 papers

TitleStatusHype
Event Retrieval Using Motion Barcodes0
EveTAR: Building a Large-Scale Multi-Task Test Collection over Arabic Tweets0
Cross-lingual Data Augmentation for Document-grounded Dialog Systems in Low Resource Languages0
A Multi-level Distillation based Dense Passage Retrieval Model0
Cross-lingual Cross-modal Pretraining for Multimodal Retrieval0
Cross-Lingual Cross-Modal Consolidation for Effective Multilingual Video Corpus Moment Retrieval0
Augmenting Low-Resource Text Classification with Graph-Grounded Pre-training and Prompting0
Adaptive Representations for Tracking Breaking News on Twitter0
Augmenting Language Models with Long-Term Memory0
A Multi-level Alignment Training Scheme for Video-and-Language Grounding0
Cross-lingual Adaptation for Recipe Retrieval with Mixup0
Cross-Lingual Ability of Multilingual Masked Language Models: A Study of Language Structure0
Augmenting Images for ASR and TTS through Single-loop and Dual-loop Multimodal Chain Framework0
Learning Effective Representations for Retrieval Using Self-Distillation with Adaptive Relevance Margins0
Event Presence Prediction Helps Trigger Detection Across Languages0
Evidence-based Trustworthiness0
Cross-language Sentence Selection via Data Augmentation and Rationale Training0
Cross-language Information Retrieval0
Augmenting End-to-End Dialog Systems with Commonsense Knowledge0
Cross Knowledge-based Generative Zero-Shot Learning Approach with Taxonomy Regularization0
A Multigraph Representation for Improved Unsupervised/Semi-supervised Learning of Human Actions0
Event Extraction in Video Transcripts0
Crossing Variational Autoencoders for Answer Retrieval0
Augmenting Document Representations for Dense Retrieval with Interpolation and Perturbation0
Distilling Knowledge from Text-to-Image Generative Models Improves Visio-Linguistic Reasoning in CLIP0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1BM25SQueries per second183.53Unverified
2ElasticsearchQueries per second21.8Unverified
3BM25-PTQueries per second6.49Unverified
4Rank-BM25Queries per second1.18Unverified
#ModelMetricClaimedVerifiedStatus
1BM25SQueries per second20.88Unverified
2ElasticsearchQueries per second7.11Unverified
3Rank-BM25Queries per second0.04Unverified
#ModelMetricClaimedVerifiedStatus
1BM25SQueries per second41.85Unverified
2ElasticsearchQueries per second12.16Unverified
3Rank-BM25Queries per second0.1Unverified
#ModelMetricClaimedVerifiedStatus
1FLMRRecall@589.32Unverified
2RA-VQARecall@582.84Unverified
#ModelMetricClaimedVerifiedStatus
1PreFLMRRecall@562.1Unverified
#ModelMetricClaimedVerifiedStatus
1CLIP-KIStext-to-video Mean Rank30Unverified
#ModelMetricClaimedVerifiedStatus
1CLIP4OutfitRecall@57.59Unverified
#ModelMetricClaimedVerifiedStatus
1MetaGen Blended RAGAccuracy (Top-1)82.1Unverified
#ModelMetricClaimedVerifiedStatus
1MetaGen Blended RAGAccuracy (Top-1)82.1Unverified
#ModelMetricClaimedVerifiedStatus
1COLTCOMP@84.55Unverified
#ModelMetricClaimedVerifiedStatus
1hello0L1,121,222Unverified