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 4976–5000 of 14297 papers

TitleStatusHype
A Study of Context Dependencies in Multi-page Product Search—0
A Latent Clothing Attribute Approach for Human Pose Estimation—0
Compositional De-Attention Networks—0
A Study of Association Measures and their Combination for Arabic MWT Extraction—0
From Mapping to Composing: A Two-Stage Framework for Zero-shot Composed Image Retrieval—0
From Models to Systems: A Comprehensive Fairness Framework for Compositional Recommender Systems—0
From RAG to Agentic: Validating Islamic-Medicine Responses with LLM Agents—0
From Zero to Hero: How local curvature at artless initial conditions leads away from bad minima—0
Further Theoretical Study of Distribution Separation Method for Information Retrieval—0
A Structure-Aware Method for Direct Pose Estimation—0
Composite Correlation Quantization for Efficient Multimodal Retrieval—0
Composite Code Sparse Autoencoders for first stage retrieval—0
A Late Fusion Framework with Multiple Optimization Methods for Media Interestingness—0
Composing Open-domain Vision with RAG for Ocean Monitoring and Conservation—0
Compose Like Humans: Jointly Improving the Coherence and Novelty for Modern Chinese Poetry Generation—0
A Stream Learning Approach for Real-Time Identification of False Data Injection Attacks in Cyber-Physical Power Systems—0
Actively Seeking and Learning from Live Data—0
Composed Query Image Retrieval Using Locally Bounded Features—0
A stochastic alternating minimizing method for sparse phase retrieval—0
AsthmaBot: Multi-modal, Multi-Lingual Retrieval Augmented Generation For Asthma Patient Support—0
Auto-ACD: A Large-scale Dataset for Audio-Language Representation Learning—0
A STEP towards Interpretable Multi-Hop Reasoning:Bridge Phrase Identification and Query Expansion—0
ABC: Achieving Better Control of Multimodal Embeddings using VLMs—0
Knowing When to Ask -- Bridging Large Language Models and Data—0
From Isolated Conversations to Hierarchical Schemas: Dynamic Tree Memory Representation for LLMs—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