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 6451–6475 of 14297 papers

TitleStatusHype
PolQA: Polish Question Answering Dataset—0
Improving RAG Retrieval via Propositional Content Extraction: a Speech Act Theory Approach—0
Exploring Content Based Image Retrieval for Highly Imbalanced Melanoma Data using Style Transfer, Semantic Image Segmentation and Ensemble Learning—0
Improving Raw Image Storage Efficiency by Exploiting Similarity—0
Cloud detection machine learning algorithms for PROBA-V—0
Exploring Confidence Measures for Word Spotting in Heterogeneous Datasets—0
Improving Retrieval Augmented Language Model with Self-Reasoning—0
Exploring Combinations of Ontological Features and Keywords for Text Retrieval—0
Improving Retrieval Augmented Neural Machine Translation by Controlling Source and Fuzzy-Match Interactions—0
Improving Retrieval-Augmented Neural Machine Translation with Monolingual Data—0
CSDR-BERT: a pre-trained scientific dataset match model for Chinese Scientific Dataset Retrieval—0
Improving Retrieval-augmented Text-to-SQL with AST-based Ranking and Schema Pruning—0
Cloud-Based Real-Time Molecular Screening Platform with MolFormer—0
Improving Retrieval for RAG based Question Answering Models on Financial Documents—0
Improving Retrieval in Sponsored Search by Leveraging Query Context Signals—0
Artefact Retrieval: Overview of NLP Models with Knowledge Base Access—0
A 106K Multi-Topic Multilingual Conversational User Dataset with Emoticons—0
Improving Robustness of Retrieval Augmented Translation via Shuffling of Suggestions—0
Exploring ChatGPT for Next-generation Information Retrieval: Opportunities and Challenges—0
Improving Scientific Document Retrieval with Concept Coverage-based Query Set Generation—0
CSMF: Cascaded Selective Mask Fine-Tuning for Multi-Objective Embedding-Based Retrieval—0
Automatic cognate identification with gap-weighted string subsequences.—0
Improving Sparse Associative Memories by Escaping from Bogus Fixed Points—0
Improving Spatiotemporal Self-Supervision by Deep Reinforcement Learning—0
Clothing Retrieval with Visual Attention Model—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