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 6401–6425 of 14297 papers

TitleStatusHype
Automated Conversion of Static to Dynamic Scheduler via Natural Language—0
Improving Entity Retrieval on Structured Data—0
Improving embedding with contrastive fine-tuning on small datasets with expert-augmented scores—0
Improving Embedding-based Large-scale Retrieval via Label Enhancement—0
Improving Embedding Accuracy for Document Retrieval Using Entity Relationship Maps and Model-Aware Contrastive Sampling—0
Cross-modal Retrieval with Improved Graph Convolution—0
Automated Code Generation and Validation for Software Components of Microcontrollers—0
Improving Few-Shot Cross-Domain Named Entity Recognition by Instruction Tuning a Word-Embedding based Retrieval Augmented Large Language Model—0
An Active Learning Based Approach For Effective Video Annotation And Retrieval—0
Improving Grapheme-to-Phoneme Conversion through In-Context Knowledge Retrieval with Large Language Models—0
Adaptive Two-Phase Finetuning LLMs for Japanese Legal Text Retrieval—0
Accelerating Inference of Retrieval-Augmented Generation via Sparse Context Selection—0
Improve Rule Retrieval and Reasoning with Self-Induction and Relevance ReEstimate—0
Improving information retrieval from electronic health records using dynamic and multi-collaborative filtering—0
Simple but Efficient: A Multi-Scenario Nearline Retrieval Framework for Recommendation on Taobao—0
Improving information retrieval through correspondence analysis instead of latent semantic analysis—0
Improving Knowledge-Aware Dialogue Response Generation by Using Human-Written Prototype Dialogues—0
Cross-Modal Retrieval with Implicit Concept Association—0
Improving Language Models via Plug-and-Play Retrieval Feedback—0
Improving Large-Scale Fact-Checking using Decomposable Attention Models and Lexical Tagging—0
Improving Legal Case Retrieval with Brain Signals—0
Improving Legal Information Retrieval by Distributional Composition with Term Order Probabilities—0
Improving Dual-Encoder Training through Dynamic Indexes for Negative Mining—0
Improving Domain-Specific Retrieval by NLI Fine-Tuning—0
Cross-Modal Retrieval Meets Inference:Improving Zero-Shot Classification with Cross-Modal Retrieval—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