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 1376–1400 of 14297 papers

TitleStatusHype
Who Does the Giant Number Pile Like Best: Analyzing Fairness in Hiring ContextsCode0
Re-ranking the Context for Multimodal Retrieval Augmented Generation—0
Knowledge Retrieval Based on Generative AI—0
Detection, Retrieval, and Explanation Unified: A Violence Detection System Based on Knowledge Graphs and GAT—0
Retrieval-Augmented Generation by Evidence Retroactivity in LLMs—0
Multilingual Open QA on the MIA Shared Task—0
The generalized phase retrieval problem over compact groups—0
Towards Reliable Testing for Multiple Information Retrieval System ComparisonsCode0
SenseRAG: Constructing Environmental Knowledge Bases with Proactive Querying for LLM-Based Autonomous Driving—0
RAG-Check: Evaluating Multimodal Retrieval Augmented Generation Performance—0
Multimodal Multihop Source Retrieval for Web Question Answering—0
TACLR: A Scalable and Efficient Retrieval-based Method for Industrial Product Attribute Value IdentificationCode0
MTRAG: A Multi-Turn Conversational Benchmark for Evaluating Retrieval-Augmented Generation SystemsCode2
Sustainable Digitalization of Business with Multi-Agent RAG and LLM—0
Political Events using RAG with LLMs—0
GeAR: Generation Augmented Retrieval—0
FlippedRAG: Black-Box Opinion Manipulation Adversarial Attacks to Retrieval-Augmented Generation Models—0
DDRM-PR: Fourier Phase Retrieval using Denoising Diffusion Restoration Models—0
Tree-based RAG-Agent Recommendation System: A Case Study in Medical Test Data—0
QuIM-RAG: Advancing Retrieval-Augmented Generation with Inverted Question Matching for Enhanced QA Performance—0
Graph-based Retrieval Augmented Generation for Dynamic Few-shot Text Classification—0
Key-value memory in the brainCode1
Towards Omni-RAG: Comprehensive Retrieval-Augmented Generation for Large Language Models in Medical Applications—0
GenTREC: The First Test Collection Generated by Large Language Models for Evaluating Information Retrieval Systems—0
Interactive Information Need Prediction with Intent and Context—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