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 6926–6950 of 14297 papers

TitleStatusHype
A Study on Broadcast Networks for Music Genre ClassificationCode0
Spectrum of non-Hermitian deep-Hebbian neural networks—0
Debias the Black-box: A Fair Ranking Framework via Knowledge Distillation—0
Interpreting Song Lyrics with an Audio-Informed Pre-trained Language ModelCode1
Improving Natural-Language-based Audio Retrieval with Transfer Learning and Audio & Text Augmentations—0
DPTDR: Deep Prompt Tuning for Dense Passage RetrievalCode0
MuMUR : Multilingual Multimodal Universal Retrieval—0
Retrieval-based Controllable Molecule GenerationCode1
Multi-Resolution Subspace-Based Optimization Method for the Retrieval of 2D Perfect Electric Conductors—0
ULISSE: A Tool for One-shot Sky Exploration and its Application to Active Galactic Nuclei DetectionCode0
VILT: Video Instructions Linking for Complex TasksCode1
Improving Personality Consistency in Conversation by Persona ExtendingCode0
Query-Response Interactions by Multi-tasks in Semantic Search for Chatbot Candidate Retrieval—0
Satellite Image Search in AgoraEO—0
Self-Supervised Pretraining of Graph Neural Network for the Retrieval of Related Mathematical Expressions in Scientific Articles—0
Generalized Relative Neighborhood Graph (GRNG) for Similarity Search—0
Revising Image-Text Retrieval via Multi-Modal Entailment—0
Image as a Foreign Language: BEiT Pretraining for All Vision and Vision-Language TasksCode0
A Contrastive Pre-training Approach to Learn Discriminative Autoencoder for Dense Retrieval—0
Scattered or Connected? An Optimized Parameter-efficient Tuning Approach for Information Retrieval—0
CODER: Coupled Diversity-Sensitive Momentum Contrastive Learning for Image-Text Retrieval—0
Adversarial contamination of networks in the setting of vertex nomination: a new trimming method—0
Fuse and Attend: Generalized Embedding Learning for Art and Sketches—0
Cognitive Modeling of Semantic Fluency Using Transformers—0
Test-time Training for Data-efficient UCDRCode0
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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