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 1676–1700 of 14297 papers

TitleStatusHype
A Reproducible Extraction of Training Images from Diffusion ModelsCode1
Generator-Retriever-Generator Approach for Open-Domain Question AnsweringCode1
Autoregressive Entity RetrievalCode1
Geometrically Mappable Image FeaturesCode1
Global and Local Semantic Completion Learning for Vision-Language Pre-trainingCode1
GPU-based Private Information Retrieval for On-Device Machine Learning InferenceCode1
Generative Multi-Modal Knowledge Retrieval with Large Language ModelsCode1
Generative Dense Retrieval: Memory Can Be a BurdenCode1
Generative Recommendation: Towards Next-generation Recommender ParadigmCode1
DREditor: An Time-efficient Approach for Building a Domain-specific Dense Retrieval ModelCode1
Chain-of-Skills: A Configurable Model for Open-domain Question AnsweringCode1
Generation-Augmented Retrieval for Open-domain Question AnsweringCode1
AccoMontage2: A Complete Harmonization and Accompaniment Arrangement SystemCode1
CFT-RAG: An Entity Tree Based Retrieval Augmented Generation Algorithm With Cuckoo FilterCode1
DSSL: Deep Surroundings-person Separation Learning for Text-based Person RetrievalCode1
DTGB: A Comprehensive Benchmark for Dynamic Text-Attributed GraphsCode1
Dual adversarial graph neural networks for multi-label cross-modal retrievalCode1
Memory Sharing for Large Language Model based AgentsCode1
Dual-branch Hybrid Learning Network for Unbiased Scene Graph GenerationCode1
Benchmarking Robustness of Multimodal Image-Text Models under Distribution ShiftCode1
Are Local Features All You Need for Cross-Domain Visual Place Recognition?Code1
AVLnet: Learning Audio-Visual Language Representations from Instructional VideosCode1
Chameleon: a Heterogeneous and Disaggregated Accelerator System for Retrieval-Augmented Language ModelsCode1
Generating Synthetic Documents for Cross-Encoder Re-Rankers: A Comparative Study of ChatGPT and Human ExpertsCode1
Generative Retrieval as Multi-Vector Dense RetrievalCode1
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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