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 5251–5275 of 14297 papers

TitleStatusHype
Finding Salient Context based on Semantic Matching for Relevance Ranking—0
Finding Similar Exercises in Retrieval Manner—0
Finding Similar Medical Questions from Question Answering Websites—0
Findings of the 2014 Workshop on Statistical Machine Translation—0
Assessing the Robustness of Retrieval-Augmented Generation Systems in K-12 Educational Question Answering with Knowledge Discrepancies—0
ColBERT-serve: Efficient Multi-Stage Memory-Mapped Scoring—0
Minimizing low-rank models of high-order tensors: Hardness, span, tight relaxation, and applications—0
Find The Gap: Knowledge Base Reasoning For Visual Question Answering—0
A + B: A General Generator-Reader Framework for Optimizing LLMs to Unleash Synergy Potential—0
Find your Needle: Small Object Image Retrieval via Multi-Object Attention Optimization—0
AskMe: A LAPPS Grid-based NLP Query and Retrieval System for Covid-19 Literature—0
FineFilter: A Fine-grained Noise Filtering Mechanism for Retrieval-Augmented Large Language Models—0
AI Recommendation System for Enhanced Customer Experience: A Novel Image-to-Text Method—0
Fast Metric Learning For Deep Neural Networks—0
Compact Deep Aggregation for Set Retrieval—0
Fine-Grained Distillation for Long Document Retrieval—0
Fine-Grained Guidance for Retrievers: Leveraging LLMs' Feedback in Retrieval-Augmented Generation—0
Fine-grained Hallucination Detection and Editing for Language Models—0
Fine-Grained Histopathological Image Analysis via Robust Segmentation and Large-Scale Retrieval—0
Fine-Grained Image Analysis with Deep Learning: A Survey—0
Fast Loop Closure Detection via Binary Content—0
Fast Kronecker product kernel methods via generalized vec trick—0
Asking More Informative Questions for Grounded Retrieval—0
GH-Feat: Learning Versatile Generative Hierarchical Features from GANs—0
Fast Interactive Image Retrieval using large-scale unlabeled data—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