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 6776–6800 of 14297 papers

TitleStatusHype
Semantic Content Prediction for Generating Interviewing Dialogues to Elicit Users’ Food Preferences—0
Repo4QA: Answering Coding Questions via Dense Retrieval on GitHub RepositoriesCode0
Reciprocal Learning of Knowledge Retriever and Response Ranker for Knowledge-Grounded Conversations—0
Event Extraction in Video Transcripts—0
CitRet: A Hybrid Model for Cited Text Span RetrievalCode0
Efficient Multilingual Multi-modal Pre-training through Triple Contrastive Loss—0
Zemi: Learning Zero-Shot Semi-Parametric Language Models from Multiple TasksCode1
Medical Question Understanding and Answering with Knowledge Grounding and Semantic Self-SupervisionCode0
SmallCap: Lightweight Image Captioning Prompted with Retrieval AugmentationCode1
DecAF: Joint Decoding of Answers and Logical Forms for Question Answering over Knowledge BasesCode1
Zero-Shot Retrieval with Search Agents and Hybrid Environments—0
ERNIE-ViL 2.0: Multi-view Contrastive Learning for Image-Text Pre-training—0
Guided Unsupervised Learning by Subaperture Decomposition for Ocean SAR Image Retrieval—0
Diffusion Posterior Sampling for General Noisy Inverse ProblemsCode2
Re-Imagen: Retrieval-Augmented Text-to-Image Generator—0
REST: REtrieve & Self-Train for generative action recognition—0
Generate-and-Retrieve: use your predictions to improve retrieval for semantic parsing—0
Evaluating Research Dataset Recommendations in a Living Lab—0
Multi-stage Information Retrieval for Vietnamese Legal Texts—0
Mr. Right: Multimodal Retrieval on Representation of ImaGe witH TextCode1
The Chamber Ensemble Generator: Limitless High-Quality MIR Data via Generative ModelingCode1
Weighted Contrastive HashingCode0
TokenFlow: Rethinking Fine-grained Cross-modal Alignment in Vision-Language Retrieval—0
Audio Retrieval with WavText5K and CLAP TrainingCode1
SEMICON: A Learning-to-hash Solution for Large-scale Fine-grained Image RetrievalCode0
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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