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 9001–9025 of 14297 papers

TitleStatusHype
FiDO: Fusion-in-Decoder optimized for stronger performance and faster inference—0
Combining semantic search and twin product classification for recognition of purchasable items in voice shopping—0
Assessing Effectiveness of Using Internal Signals for Check-Worthy Claim Identification in Unlabeled Data for Automated Fact-Checking—0
A Kind of Affine Weighted Moment Invariants—0
FiD-Light: Efficient and Effective Retrieval-Augmented Text Generation—0
Fidelity-Weighted Learning—0
Combining Real-Valued and Binary Gabor-Radon Features for Classification and Search in Medical Imaging Archives—0
FiDeLiS: Faithful Reasoning in Large Language Model for Knowledge Graph Question Answering—0
Assessing Brittleness of Image-Text Retrieval Benchmarks from Vision-Language Models Perspective—0
Combining Q&A Pair Quality and Question Relevance Features on Community-based Question Retrieval—0
FFF: Fixing Flawed Foundations in contrastive pre-training results in very strong Vision-Language models—0
Few-shot Single-view 3D Reconstruction with Memory Prior Contrastive Network—0
Combining Language and Vision with a Multimodal Skip-gram Model—0
Assessing Algorithmic Biases for Musical Version Identification—0
A Keypoint-based Morphological Signature for Large-scale Neuroimage Analysis—0
Activation-aware Probe-Query: Effective Key-Value Retrieval for Long-Context LLMs Inference—0
A Bayesian Analysis of Dynamics in Free Recall—0
Few-shot Prompting for Pairwise Ranking: An Effective Non-Parametric Retrieval Model—0
Few-shot Metric Learning: Online Adaptation of Embedding for Retrieval—0
Few-Shot Meta Learning for Recognizing Facial Phenotypes of Genetic Disorders—0
Combining Deep Neural Reranking and Unsupervised Extraction for Multi-Query Focused Summarization—0
Few-Shot Learning Through an Information Retrieval Lens—0
Few-shot Intent Classification and Slot Filling with Retrieved Examples—0
Combining data assimilation and machine learning to emulate a dynamical model from sparse and noisy observations: a case study with the Lorenz 96 model—0
ASRank: Zero-Shot Re-Ranking with Answer Scent for Document 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