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 4926–4950 of 14297 papers

TitleStatusHype
Computing with Cognitive States—0
A Supervised Model for Extraction of Multiword Expressions, Based on Statistical Context Features—0
Fractional Local Neighborhood Intensity Pattern for Image Retrieval using Genetic Algorithm—0
Computing Lexical Contrast—0
Computationally Efficient Labeling of Cancer Related Forum Posts by Non-Clinical Text Information Retrieval—0
A Summary of the ALQAC 2021 Competition—0
Computationally Bounded Retrieval—0
A Sub-block Based Image Retrieval Using Modified Integrated Region Matching—0
Align, Adapt and Inject: Sound-guided Unified Image Generation—0
FRAG: A Flexible Modular Framework for Retrieval-Augmented Generation based on Knowledge Graphs—0
FREESON: Retriever-Free Retrieval-Augmented Reasoning via Corpus-Traversing MCTS—0
Computational Approaches for App-to-App Retrieval and Design Consistency Check—0
Astute RAG: Overcoming Imperfect Retrieval Augmentation and Knowledge Conflicts for Large Language Models—0
Compressive Shift Retrieval—0
Compressive Quantization for Fast Object Instance Search in Videos—0
A Study on Using Transfer Learning to Improve BERT Model for Emotional Classification of Chinese Lyrics—0
Align2Ground: Weakly Supervised Phrase Grounding Guided by Image-Caption Alignment—0
Compressive Phase Retrieval: Optimal Sample Complexity with Deep Generative Priors—0
Compressive Change Retrieval for Moving Object Detection—0
A Study on the usage of Data Structures in Information Retrieval—0
Compression of Deep Neural Networks for Image Instance Retrieval—0
Compression of Deep Learning Models for Text: A Survey—0
A study on the Interpretability of Neural Retrieval Models using DeepSHAP—0
ACTNET: end-to-end learning of feature activations and multi-stream aggregation for effective instance image retrieval—0
A Simplified Retriever to Improve Accuracy of Phenotype Normalizations by Large Language Models—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