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 3376–3400 of 14297 papers

TitleStatusHype
Decoding BACnet Packets: A Large Language Model Approach for Packet Interpretation—0
Autonomous Radiotherapy Treatment Planning Using DOLA: A Privacy-Preserving, LLM-Based Optimization Agent—0
Decoding Working Memory Load from EEG with LSTM Networks—0
DAT: Dynamic Alpha Tuning for Hybrid Retrieval in Retrieval-Augmented Generation—0
Decompose, Enrich, and Extract! Schema-aware Event Extraction using LLMs—0
Decompose Semantic Shifts for Composed Image Retrieval—0
Decomposing Complex Queries for Tip-of-the-tongue Retrieval—0
Decomposing Normal and Abnormal Features of Medical Images for Content-based Image Retrieval—0
Decomposing Normal and Abnormal Features of Medical Images into Discrete Latent Codes for Content-Based Image Retrieval—0
Analyzing Local Representations of Self-supervised Vision Transformers—0
Deconfounded and Explainable Interactive Vision-Language Retrieval of Complex Scenes—0
Dataset vs Reality: Understanding Model Performance from the Perspective of Information Need—0
Datasets for Multilingual Answer Sentence Selection—0
Decorum: A Language-Based Approach For Style-Conditioned Synthesis of Indoor 3D Scenes—0
Decoupled Context Processing for Context Augmented Language Modeling—0
Semantic Caching of Contextual Summaries for Efficient Question-Answering with Language Models—0
Diversity driven Query Rewriting in Search Advertising—0
Decoupling Knowledge and Reasoning in Transformers: A Modular Architecture with Generalized Cross-Attention—0
Auto-MVCNN: Neural Architecture Search for Multi-view 3D Shape Recognition—0
Dataset Protection via Watermarked Canaries in Retrieval-Augmented LLMs—0
Distributional Vision-Language Alignment by Cauchy-Schwarz Divergence—0
Decoy Effect In Search Interaction: Understanding User Behavior and Measuring System Vulnerability—0
Decoy Effect in Search Interaction: A Pilot Study—0
A weakly supervised adaptive triplet loss for deep metric learning—0
Dataset of Natural Language Queries for E-Commerce—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