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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 2926–2950 of 14297 papers

TitleStatusHype
Cross-modal RAG: Sub-dimensional Retrieval-Augmented Text-to-Image GenerationCode0
Logical Consistency is Vital: Neural-Symbolic Information Retrieval for Negative-Constraint QueriesCode0
LegalSearchLM: Rethinking Legal Case Retrieval as Legal Elements Generation—0
DocReRank: Single-Page Hard Negative Query Generation for Training Multi-Modal RAG Rerankers—0
SkewRoute: Training-Free LLM Routing for Knowledge Graph Retrieval-Augmented Generation via Score Skewness of Retrieved Context—0
Climate Finance BenchCode0
Agent-UniRAG: A Trainable Open-Source LLM Agent Framework for Unified Retrieval-Augmented Generation Systems—0
Yambda-5B -- A Large-Scale Multi-modal Dataset for Ranking And Retrieval—0
Aligning Proteins and Language: A Foundation Model for Protein Retrieval—0
Rethinking Chunk Size For Long-Document Retrieval: A Multi-Dataset AnalysisCode0
AI-Supported Platform for System Monitoring and Decision-Making in Nuclear Waste Management with Large Language Models—0
RelationalFactQA: A Benchmark for Evaluating Tabular Fact Retrieval from Large Language Models—0
Memorization to Generalization: Emergence of Diffusion Models from Associative Memory—0
Retrieval Visual Contrastive Decoding to Mitigate Object Hallucinations in Large Vision-Language ModelsCode0
GenKI: Enhancing Open-Domain Question Answering with Knowledge Integration and Controllable Generation in Large Language ModelsCode0
MLLM-Guided VLM Fine-Tuning with Joint Inference for Zero-Shot Composed Image Retrieval—0
DoctorRAG: Medical RAG Fusing Knowledge with Patient Analogy through Textual Gradients—0
MA-RAG: Multi-Agent Retrieval-Augmented Generation via Collaborative Chain-of-Thought Reasoning—0
Learning to Select In-Context Demonstration Preferred by Large Language Model—0
LogiCoL: Logically-Informed Contrastive Learning for Set-based Dense RetrievalCode0
Effectiveness of Prompt Optimization in NL2SQL Systems—0
DGRAG: Distributed Graph-based Retrieval-Augmented Generation in Edge-Cloud Systems—0
LLM Web Dynamics: Tracing Model Collapse in a Network of LLMs—0
Languages in Multilingual Speech Foundation Models Align Both Phonetically and Semantically—0
It's High Time: A Survey of Temporal Information Retrieval and Question Answering—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