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 176–200 of 14297 papers

TitleStatusHype
Bridging Language and Items for Retrieval and RecommendationCode3
Can Mamba Learn How to Learn? A Comparative Study on In-Context Learning TasksCode3
Adaptive-RAG: Learning to Adapt Retrieval-Augmented Large Language Models through Question ComplexityCode3
BMX: Entropy-weighted Similarity and Semantic-enhanced Lexical SearchCode3
HyperGraphRAG: Retrieval-Augmented Generation with Hypergraph-Structured Knowledge RepresentationCode3
M3D: Advancing 3D Medical Image Analysis with Multi-Modal Large Language ModelsCode3
PoisonedRAG: Knowledge Corruption Attacks to Retrieval-Augmented Generation of Large Language ModelsCode3
Beyond Quacking: Deep Integration of Language Models and RAG into DuckDBCode3
Hierarchical Lexical Graph for Enhanced Multi-Hop RetrievalCode3
HtmlRAG: HTML is Better Than Plain Text for Modeling Retrieved Knowledge in RAG SystemsCode3
GRAG: Graph Retrieval-Augmented GenerationCode3
Benchmarking Multimodal Retrieval Augmented Generation with Dynamic VQA Dataset and Self-adaptive Planning AgentCode3
Direct Retrieval-augmented Optimization: Synergizing Knowledge Selection and Language ModelsCode3
BERGEN: A Benchmarking Library for Retrieval-Augmented GenerationCode3
Graph Retrieval-Augmented Generation: A SurveyCode3
Gemini 1.5: Unlocking multimodal understanding across millions of tokens of contextCode3
GFM-RAG: Graph Foundation Model for Retrieval Augmented GenerationCode3
AutoSurvey: Large Language Models Can Automatically Write SurveysCode3
AvaTaR: Optimizing LLM Agents for Tool Usage via Contrastive ReasoningCode3
GNN-RAG: Graph Neural Retrieval for Large Language Model ReasoningCode3
From Matching to Generation: A Survey on Generative Information RetrievalCode3
A Comprehensive Survey on Composed Image RetrievalCode3
Auto-RAG: Autonomous Retrieval-Augmented Generation for Large Language ModelsCode3
Human-like Episodic Memory for Infinite Context LLMsCode3
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