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 44264450 of 14297 papers

TitleStatusHype
IDEAL: Independent Domain Embedding Augmentation LearningCode0
CHEF: Cross-modal Hierarchical Embeddings for Food Domain RetrievalCode0
Hybrid Style Siamese Network: Incorporating style loss in complementary apparels retrievalCode0
Hybrid Losses for Hierarchical Embedding LearningCode0
Check_square at CheckThat! 2020: Claim Detection in Social Media via Fusion of Transformer and Syntactic FeaturesCode0
A Geometric Analysis of Phase RetrievalCode0
HybridCite: A Hybrid Model for Context-Aware Citation RecommendationCode0
Human Preferences as Dueling BanditsCode0
Are Large Language Models Good at Utility Judgments?Code0
HUSE: Hierarchical Universal Semantic EmbeddingsCode0
Cheap and Good? Simple and Effective Data Augmentation for Low Resource Machine ReadingCode0
A Regularized Conditional GAN for Posterior Sampling in Image Recovery ProblemsCode0
Human Motion Analysis with Deep Metric LearningCode0
Hybrid and Collaborative Passage RerankingCode0
Human-in-the-loop Machine Translation with Large Language ModelCode0
Hubness Reduction Improves Sentence-BERT Semantic SpacesCode0
Hubless Nearest Neighbor Search for Bilingual Lexicon InductionCode0
Hybrid Approximate Nearest Neighbor Indexing and Search (HANNIS) for Large Descriptor DatabasesCode0
ChatSearch: a Dataset and a Generative Retrieval Model for General Conversational Image RetrievalCode0
HRDE: Retrieval-Augmented Large Language Models for Chinese Health Rumor Detection and ExplainabilityCode0
How to Leverage Personal Textual Knowledge for Personalized Conversational Information RetrievalCode0
How Train-Test Leakage Affects Zero-shot RetrievalCode0
ChatGPT for GTFS: Benchmarking LLMs on GTFS Understanding and RetrievalCode0
Are All Combinations Equal? Combining Textual and Visual Features with Multiple Space Learning for Text-Based Video RetrievalCode0
A Reality Check on Context Utilisation for Retrieval-Augmented GenerationCode0
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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