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 5176–5200 of 14297 papers

TitleStatusHype
Feature overwriting as a finite mixture process: Evidence from comprehension data—0
Feature Pyramid Hashing—0
Ask Me What You Need: Product Retrieval using Knowledge from GPT-3—0
Generative Query Reformulation for Effective Adhoc Search—0
Feature Selection and Model Comparison on Microsoft Learning-to-Rank Data Sets—0
Feature Selection for Better Spectral Characterization or: How I Learned to Start Worrying and Love Ensembles—0
Features-over-the-Air: Contrastive Learning Enabled Cooperative Edge Inference—0
Generative Relevance Feedback with Large Language Models—0
Generative Retrieval as Dense Retrieval—0
FecTek: Enhancing Term Weight in Lexicon-Based Retrieval with Feature Context and Term-level Knowledge—0
Generative Retrieval Meets Multi-Graded Relevance—0
Federated Neural Graph Databases—0
Federated Learning and RAG Integration: A Scalable Approach for Medical Large Language Models—0
Federated One-Shot Learning with Data Privacy and Objective-Hiding—0
Fast, Small, and Simple Document Listing on Repetitive Text Collections—0
CoLe and LYS at BioASQ MESINESP8 Task: similarity based descriptor assignment in Spanish—0
Federated Retrieval Augmented Generation for Multi-Product Question Answering—0
Fast & Slow Learning: Incorporating Synthetic Gradients in Neural Memory Controllers—0
Fast Slate Policy Optimization: Going Beyond Plackett-Luce—0
FedPandemic: A Cross-Device Federated Learning Approach Towards Elementary Prognosis of Diseases During a Pandemic—0
FedPerm: Private and Robust Federated Learning by Parameter Permutation—0
Cold Start Problem For Automated Live Video Comments—0
COM3D: Leveraging Cross-View Correspondence and Cross-Modal Mining for 3D Retrieval—0
FedVMR: A New Federated Learning method for Video Moment Retrieval—0
Ask Me Anything in Your Native Language—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