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 5376–5400 of 14297 papers

TitleStatusHype
Boosted Dense Retriever—0
Annotation of Computer Science Papers for Semantic Relation Extrac-tion—0
DM2RM: Dual-Mode Multimodal Ranking for Target Objects and Receptacles Based on Open-Vocabulary Instructions—0
Boosted Dense Retriever—0
DLocRL: A Deep Learning Pipeline for Fine-Grained Location Recognition and Linking in Tweets—0
DLIP: Distilling Language-Image Pre-training—0
BoolQuestions: Does Dense Retrieval Understand Boolean Logic in Language?—0
Annotation-free Learning of Deep Representations for Word Spotting using Synthetic Data and Self Labeling—0
Adversarial Attack on Deep Product Quantization Network for Image Retrieval—0
DKPLM: Decomposable Knowledge-enhanced Pre-trained Language Model for Natural Language Understanding—0
Boolean-aware Attention for Dense Retrieval—0
DIVKNOWQA: Assessing the Reasoning Ability of LLMs via Open-Domain Question Answering over Knowledge Base and Text—0
Annotation Cost-Efficient Active Learning for Deep Metric Learning Driven Remote Sensing Image Retrieval—0
Dividing and Conquering Cross-Modal Recipe Retrieval: from Nearest Neighbours Baselines to SoTA—0
Divide & Conquer for Entailment-aware Multi-hop Evidence Retrieval—0
Book Review: Graph-Based Natural Language Processing and Information Retrieval by Rada Mihalcea and Dragomir Radev—0
BookQA: Stories of Challenges and Opportunities—0
Annotation Cost Efficient Active Learning for Content Based Image Retrieval—0
Adversarial Attack on Deep Cross-Modal Hamming Retrieval—0
A Comparative Analysis of Retrievability and PageRank Measures—0
3D-2D Neural Nets for Phase Retrieval in Noisy Interferometric Imaging—0
Content-based image retrieval using Mix histogram—0
Divide by Question, Conquer by Agent: SPLIT-RAG with Question-Driven Graph Partitioning—0
Divide and Rule: Effective Pre-Training for Context-Aware Multi-Encoder Translation Models—0
Divide and Conquer: Towards Better Embedding-based Retrieval for Recommender Systems From a Multi-task Perspective—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