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 2176–2200 of 14297 papers

TitleStatusHype
CCL: Continual Contrastive Learning for LiDAR Place RecognitionCode1
DEXTER: A Benchmark for open-domain Complex Question Answering using LLMsCode1
DGL: Dynamic Global-Local Prompt Tuning for Text-Video RetrievalCode1
Correlation Verification for Image RetrievalCode1
CaseGNN++: Graph Contrastive Learning for Legal Case Retrieval with Graph AugmentationCode1
Dialog Inpainting: Turning Documents into DialogsCode1
A Comparison of Pre-trained Vision-and-Language Models for Multimodal Representation Learning across Medical Images and ReportsCode1
Corpus-Steered Query Expansion with Large Language ModelsCode1
Dimension Reduction for Efficient Dense Retrieval via Conditional AutoencoderCode1
A Comprehensive Assessment of Dialog Evaluation MetricsCode1
CoRT: Complementary Rankings from TransformersCode1
CRAFT Your Dataset: Task-Specific Synthetic Dataset Generation Through Corpus Retrieval and AugmentationCode1
CORD-19: The COVID-19 Open Research DatasetCode1
Discovering Mathematical Objects of Interest -- A Study of Mathematical NotationsCode1
DiscoverPath: A Knowledge Refinement and Retrieval System for Interdisciplinarity on Biomedical ResearchCode1
Answer Mining from a Pool of Images: Towards Retrieval-Based Visual Question AnsweringCode1
Knowledge Card: Filling LLMs' Knowledge Gaps with Plug-in Specialized Language ModelsCode1
Enhancing Recipe Retrieval with Foundation Models: A Data Augmentation PerspectiveCode1
CARE: a Benchmark Suite for the Classification and Retrieval of EnzymesCode1
CorpusBrain: Pre-train a Generative Retrieval Model for Knowledge-Intensive Language TasksCode1
Data Curation Alone Can Stabilize In-context LearningCode1
Conversational Word Embedding for Retrieval-Based Dialog SystemCode1
Distantly-Supervised Evidence Retrieval Enables Question Answering without Evidence AnnotationCode1
A Comprehensive Empirical Study of Vision-Language Pre-trained Model for Supervised Cross-Modal RetrievalCode1
Conversational Question Answering over Passages by Leveraging Word Proximity NetworksCode1
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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