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 1976–2000 of 14297 papers

TitleStatusHype
A Prior Instruction Representation Framework for Remote Sensing Image-text RetrievalCode1
BMRetriever: Tuning Large Language Models as Better Biomedical Text RetrieversCode1
Cross-modal Contrastive Learning for Speech TranslationCode1
Cross-Modal Retrieval: A Systematic Review of Methods and Future DirectionsCode1
Cross-domain Retrieval in the Legal and Patent Domains: a Reproducibility StudyCode1
Cross-Lingual Cross-Modal Retrieval with Noise-Robust LearningCode1
Semantic-Fused Multi-Granularity Cross-City Traffic PredictionCode1
Cross-Batch Memory for Embedding LearningCode1
Cross-document Event Coreference Search: Task, Dataset and ModelingCode1
Cross-Modal Adapter for Text-Video RetrievalCode1
CLASP: Contrastive Language-Speech Pretraining for Multilingual Multimodal Information RetrievalCode1
Cross-modal Retrieval for Knowledge-based Visual Question AnsweringCode1
CSFCube -- A Test Collection of Computer Science Research Articles for Faceted Query by ExampleCode1
RankDNN: Learning to Rank for Few-shot LearningCode1
Ranked List Truncation for Large Language Model-based Re-RankingCode1
DeCLUTR: Deep Contrastive Learning for Unsupervised Textual RepresentationsCode1
CityFlow-NL: Tracking and Retrieval of Vehicles at City Scale by Natural Language DescriptionsCode1
Creating Something from Nothing: Unsupervised Knowledge Distillation for Cross-Modal HashingCode1
C-RAG: Certified Generation Risks for Retrieval-Augmented Language ModelsCode1
An Objective Metric for Explainable AI: How and Why to Estimate the Degree of ExplainabilityCode1
CREPE: A Convolutional Representation for Pitch EstimationCode1
RARE: Retrieval-Augmented Reasoning Enhancement for Large Language ModelsCode1
CoVR-2: Automatic Data Construction for Composed Video RetrievalCode1
CRAFT Your Dataset: Task-Specific Synthetic Dataset Generation Through Corpus Retrieval and AugmentationCode1
CREPE: Can Vision-Language Foundation Models Reason Compositionally?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