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 4976–5000 of 14297 papers

TitleStatusHype
Generative Adversarial User Privacy in Lossy Single-Server Information RetrievalCode0
Machine learning for music genre: multifaceted review and experimentation with audiosetCode0
Machine Reading of Historical EventsCode0
Exploring the Representation Power of SPLADE ModelsCode0
Geometric features for voxel-based surface recognitionCode0
Exploring the Temporal Cues to Enhance Video Retrieval on Standardized CDVACode0
Effective Controllable Bias Mitigation for Classification and Retrieval using Gate AdaptersCode0
Generating Biomedical Question Answering Corpora from Q&A forumsCode0
Exploring Training and Inference Scaling Laws in Generative RetrievalCode0
Generating Sentential Arguments from Diverse Perspectives on Controversial TopicCode0
MAGID: An Automated Pipeline for Generating Synthetic Multi-modal DatasetsCode0
Generating Signed Language Instructions in Large-Scale Dialogue SystemsCode0
Effective and Efficient Indexing in Cross-Modal Hashing-Based DatasetsCode0
Generating Images from Captions with AttentionCode0
Generating Diverse and Meaningful CaptionsCode0
Generating Natural Questions About an ImageCode0
Expository Text Generation: Imitate, Retrieve, ParaphraseCode0
Table2Vec: Neural Word and Entity Embeddings for Table Population and RetrievalCode0
Expressing Visual Relationships via LanguageCode0
CNN Features off-the-shelf: an Astounding Baseline for RecognitionCode0
Generalizing Conversational Dense Retrieval via LLM-Cognition Data AugmentationCode0
ExprGAN: Facial Expression Editing with Controllable Expression IntensityCode0
CNN Image Retrieval Learns from BoW: Unsupervised Fine-Tuning with Hard ExamplesCode0
An Unsupervised Normalization Algorithm for Noisy Text: A Case Study for Information Retrieval and Stance DetectionCode0
Generalizing A Person Retrieval Model Hetero- and HomogeneouslyCode0
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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