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 4926–4950 of 14297 papers

TitleStatusHype
How Can Context Help? Exploring Joint Retrieval of Passage and Personalized Context—0
Towards Food Image Retrieval via Generalization-oriented Sampling and Loss Function DesignCode0
LLM2KB: Constructing Knowledge Bases using instruction tuned context aware Large Language ModelsCode0
Preserving Modality Structure Improves Multi-Modal LearningCode0
Sentence Embedding Models for Ancient Greek Using Multilingual Knowledge DistillationCode1
Real-time Detection of AI-Generated Speech for DeepFake Voice Conversion—0
DLIP: Distilling Language-Image Pre-training—0
Parameter-Efficient Transfer Learning for Remote Sensing Image-Text RetrievalCode1
REB: Reducing Biases in Representation for Industrial Anomaly DetectionCode1
Modeling Uncertainty and Using Post-fusion as Fallback Improves Retrieval Augmented Generation with LLMs—0
Prompt2Model: Generating Deployable Models from Natural Language InstructionsCode4
Progressive Feature Mining and External Knowledge-Assisted Text-Pedestrian Image Retrieval—0
HashReID: Dynamic Network with Binary Codes for Efficient Person Re-identification—0
Reranking Passages with Coarse-to-Fine Neural Retriever Enhanced by List-Context Information—0
Learning from Negative User Feedback and Measuring Responsiveness for Sequential Recommenders—0
YAGO 4.5: A Large and Clean Knowledge Base with a Rich TaxonomyCode1
EVE: Efficient Vision-Language Pre-training with Masked Prediction and Modality-Aware MoE—0
Hybrid Retrieval and Multi-stage Text Ranking Solution at TREC 2022 Deep Learning Track—0
Knowledge Graph Prompting for Multi-Document Question AnsweringCode2
CLIP Multi-modal Hashing: A new baseline CLIPMH—0
L^2R: Lifelong Learning for First-stage Retrieval with Backward-Compatible RepresentationsCode0
Pre-training with Aspect-Content Text Mutual Prediction for Multi-Aspect Dense RetrievalCode0
Multi-event Video-Text RetrievalCode1
ViLLA: Fine-Grained Vision-Language Representation Learning from Real-World DataCode1
Composed Image Retrieval using Contrastive Learning and Task-oriented CLIP-based FeaturesCode1
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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