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 9676–9700 of 14297 papers

TitleStatusHype
Contextual Document Embeddings—0
Contextual Fine-to-Coarse Distillation for Coarse-grained Response Selection in Open-Domain Conversations—0
Contextualised Browsing in a Digital Library's Living Lab—0
Contextualization for the Organization of Text Documents Streams—0
Contextualization of ASR with LLM using phonetic retrieval-based augmentation—0
Contextualization of topics - browsing through terms, authors, journals and cluster allocations—0
Contextualized Query Embeddings for Conversational Search—0
Contextual Media Retrieval Using Natural Language Queries—0
Contextual Memory Trees—0
Contextual Mood Analysis with Knowledge Graph Representation for Hindi Song Lyrics in Devanagari Script—0
Contextual Similarity Distillation for Asymmetric Image Retrieval—0
Measuring Context-Word Biases in Lexical Semantic Datasets—0
Continual Learning for Large Language Models: A Survey—0
Continual learning in cross-modal retrieval—0
Continual Lifelong Learning with Neural Networks: A Review—0
Exploring the Practicality of Generative Retrieval on Dynamic Corpora—0
Continual Vision-Language Representation Learning with Off-Diagonal Information—0
Continuous Active Learning Using Pretrained Transformers—0
Continuous Approach to Phase (Norm) Retrieval Frames—0
Contrast & Compress: Learning Lightweight Embeddings for Short Trajectories—0
Contrastive Cross-Modal Knowledge Sharing Pre-training for Vision-Language Representation Learning and Retrieval—0
Contrastive Document Representation Learning with Graph Attention Networks—0
Contrastive Feature Masking Open-Vocabulary Vision Transformer—0
Contrastive Language, Action, and State Pre-training for Robot Learning—0
Contrastive Learning for Cross-modal Artist 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