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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 151–175 of 14297 papers

TitleStatusHype
Graph Retrieval-Augmented Generation: A SurveyCode3
OpenResearcher: Unleashing AI for Accelerated Scientific ResearchCode3
BMX: Entropy-weighted Similarity and Semantic-enhanced Lexical SearchCode3
RAGEval: Scenario Specific RAG Evaluation Dataset Generation FrameworkCode3
TaskGen: A Task-Based, Memory-Infused Agentic Framework using StrictJSONCode3
Human-like Episodic Memory for Infinite Context LLMsCode3
Scaling Retrieval-Based Language Models with a Trillion-Token DatastoreCode3
Robust Neural Information Retrieval: An Adversarial and Out-of-distribution PerspectiveCode3
Retrieval-augmented generation in multilingual settingsCode3
BERGEN: A Benchmarking Library for Retrieval-Augmented GenerationCode3
Searching for Best Practices in Retrieval-Augmented GenerationCode3
Can Long-Context Language Models Subsume Retrieval, RAG, SQL, and More?Code3
AvaTaR: Optimizing LLM Agents for Tool Usage via Contrastive ReasoningCode3
AutoSurvey: Large Language Models Can Automatically Write SurveysCode3
Multi-Head RAG: Solving Multi-Aspect Problems with LLMsCode3
GNN-RAG: Graph Neural Retrieval for Large Language Model ReasoningCode3
CHESS: Contextual Harnessing for Efficient SQL SynthesisCode3
GRAG: Graph Retrieval-Augmented GenerationCode3
MS MARCO Web Search: a Large-scale Information-rich Web Dataset with Millions of Real Click LabelsCode3
RAG and RAU: A Survey on Retrieval-Augmented Language Model in Natural Language ProcessingCode3
Retrieval Head Mechanistically Explains Long-Context FactualityCode3
From Matching to Generation: A Survey on Generative Information RetrievalCode3
STaRK: Benchmarking LLM Retrieval on Textual and Relational Knowledge BasesCode3
M3D: Advancing 3D Medical Image Analysis with Multi-Modal Large Language ModelsCode3
MagicLens: Self-Supervised Image Retrieval with Open-Ended InstructionsCode3
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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