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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 176–200 of 14297 papers

TitleStatusHype
A Comprehensive Survey on Composed Image RetrievalCode3
BMX: Entropy-weighted Similarity and Semantic-enhanced Lexical SearchCode3
Landmark Attention: Random-Access Infinite Context Length for TransformersCode3
HyperGraphRAG: Retrieval-Augmented Generation with Hypergraph-Structured Knowledge RepresentationCode3
INTERS: Unlocking the Power of Large Language Models in Search with Instruction TuningCode3
Human-like Episodic Memory for Infinite Context LLMsCode3
PoisonedRAG: Knowledge Corruption Attacks to Retrieval-Augmented Generation of Large Language ModelsCode3
Language-based Audio Moment RetrievalCode3
M3D: Advancing 3D Medical Image Analysis with Multi-Modal Large Language ModelsCode3
AlphaFin: Benchmarking Financial Analysis with Retrieval-Augmented Stock-Chain FrameworkCode3
GRAG: Graph Retrieval-Augmented GenerationCode3
Graph Retrieval-Augmented Generation: A SurveyCode3
Hierarchical Lexical Graph for Enhanced Multi-Hop RetrievalCode3
Benchmarking Multimodal Retrieval Augmented Generation with Dynamic VQA Dataset and Self-adaptive Planning AgentCode3
RAT: Retrieval Augmented Thoughts Elicit Context-Aware Reasoning in Long-Horizon GenerationCode3
BERGEN: A Benchmarking Library for Retrieval-Augmented GenerationCode3
Gemini 1.5: Unlocking multimodal understanding across millions of tokens of contextCode3
AutoSurvey: Large Language Models Can Automatically Write SurveysCode3
AvaTaR: Optimizing LLM Agents for Tool Usage via Contrastive ReasoningCode3
GFM-RAG: Graph Foundation Model for Retrieval Augmented GenerationCode3
From Matching to Generation: A Survey on Generative Information RetrievalCode3
Auto-RAG: Autonomous Retrieval-Augmented Generation for Large Language ModelsCode3
Beyond Quacking: Deep Integration of Language Models and RAG into DuckDBCode3
GNN-RAG: Graph Neural Retrieval for Large Language Model ReasoningCode3
HtmlRAG: HTML is Better Than Plain Text for Modeling Retrieved Knowledge in RAG SystemsCode3
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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