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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 1426–1450 of 14297 papers

TitleStatusHype
Learning with Noisy Triplet Correspondence for Composed Image Retrieval—0
Cross-Modal 3D Representation with Multi-View Images and Point Clouds—0
Shift the Lens: Environment-Aware Unsupervised Camouflaged Object DetectionCode0
Rethinking Noisy Video-Text Retrieval via Relation-aware Alignment—0
Bridging Modalities: Improving Universal Multimodal Retrieval by Multimodal Large Language Models—0
Incorporating Dense Knowledge Alignment into Unified Multimodal Representation Models—0
Fuzzy Multimodal Learning for Trusted Cross-modal RetrievalCode1
Generative Zero-Shot Composed Image Retrieval—0
CCIN: Compositional Conflict Identification and Neutralization for Composed Image RetrievalCode0
HORUS: Multimodal Large Language Models Framework for Video Retrieval at VBS 2025—0
TrustRAG: Enhancing Robustness and Trustworthiness in RAGCode2
Unfolding the Headline: Iterative Self-Questioning for News Retrieval and Timeline SummarizationCode3
Decoupling Knowledge and Reasoning in Transformers: A Modular Architecture with Generalized Cross-Attention—0
CancerKG.ORG A Web-scale, Interactive, Verifiable Knowledge Graph-LLM Hybrid for Assisting with Optimal Cancer Treatment and Care—0
KnowRA: Knowledge Retrieval Augmented Method for Document-level Relation Extraction with Comprehensive Reasoning Abilities—0
MAIN-RAG: Multi-Agent Filtering Retrieval-Augmented Generation—0
Retrieval-Augmented Generation with Graphs (GraphRAG)Code0
CaReBench: A Fine-Grained Benchmark for Video Captioning and Retrieval—0
RAG-Instruct: Boosting LLMs with Diverse Retrieval-Augmented InstructionsCode2
Chunk-Distilled Language Modeling—0
KAE: Kolmogorov-Arnold Auto-Encoder for Representation LearningCode1
An Overview and Discussion on Using Large Language Models for Implementation Generation of Solutions to Open-Ended Problems—0
ACL-rlg: A Dataset for Reading List Generation—0
The Text Classification Pipeline: Starting Shallow going Deeper—0
Retrieval-Augmented Generation for Mobile Edge Computing via Large Language Model—0
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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