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 10951–11000 of 14297 papers

TitleStatusHype
Fast Metric Learning For Deep Neural Networks—0
Fast Prototyping a Dialogue Comprehension System for Nurse-Patient Conversations on Symptom Monitoring—0
FastRAG: Retrieval Augmented Generation for Semi-structured Data—0
Fast, Sample-Efficient Algorithms for Structured Phase Retrieval—0
Fast Slate Policy Optimization: Going Beyond Plackett-Luce—0
Fast & Slow Learning: Incorporating Synthetic Gradients in Neural Memory Controllers—0
Fast, Small, and Simple Document Listing on Repetitive Text Collections—0
Fast Training Dataset Attribution via In-Context Learning—0
Fast Training of Triplet-based Deep Binary Embedding Networks—0
Fast Tweet Retrieval with Compact Binary Codes—0
Fast Video Moment Retrieval—0
Fast Weight Long Short-Term Memory—0
Fatigued PageRank—0
Fatigued Random Walks in Hypergraphs: A Neuronal Analogy to Improve Retrieval Performance—0
FAUST: Dataset and Evaluation for 3D Mesh Registration—0
Faux Polyglot: A Study on Information Disparity in Multilingual Large Language Models—0
FCA_Retrieval: A Multi-operator Algorithm for Information Retrieval from Binary Concept Lattice—0
FCC: Fusing Conversation History and Candidate Provenance for Contextual Response Ranking in Dialogue Systems—0
Feature Activation Map: Visual Explanation of Deep Learning Models for Image Classification—0
Feature-Based Selection of Dependency Paths in Ad Hoc Information Retrieval—0
Feature Extraction and Soft Computing Methods for Aerospace Structure Defect Classification—0
Feature Extraction in Augmented Reality—0
Feature Extraction Using Deep Generative Models for Bangla Text Classification on a New Comprehensive Dataset—0
Feature Fusion Methods for Indexing and Retrieval of Biometric Data: Application to Face Recognition with Privacy Protection—0
Feature-Independent Context Estimation for Automatic Image Annotation—0
Feature overwriting as a finite mixture process: Evidence from comprehension data—0
Feature Pyramid Hashing—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
Feature Super-Resolution: Make Machine See More Clearly—0
FeB4RAG: Evaluating Federated Search in the Context of Retrieval Augmented Generation—0
FecTek: Enhancing Term Weight in Lexicon-Based Retrieval with Feature Context and Term-level Knowledge—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
Federated Retrieval-Augmented Generation: A Systematic Mapping Study—0
Federated Retrieval Augmented Generation for Multi-Product Question Answering—0
FedHAP: Federated Hashing with Global Prototypes for Cross-silo Retrieval—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
FedVMR: A New Federated Learning method for Video Moment Retrieval—0
Fetch-A-Set: A Large-Scale OCR-Free Benchmark for Historical Document Retrieval—0
Few-shot Intent Classification and Slot Filling with Retrieved Examples—0
Few-Shot Learning Through an Information Retrieval Lens—0
Few-Shot Meta Learning for Recognizing Facial Phenotypes of Genetic Disorders—0
Few-shot Metric Learning: Online Adaptation of Embedding for Retrieval—0
Few-shot Prompting for Pairwise Ranking: An Effective Non-Parametric Retrieval Model—0
Few-shot Single-view 3D Reconstruction with Memory Prior Contrastive Network—0
FFF: Fixing Flawed Foundations in contrastive pre-training results in very strong Vision-Language models—0
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All datasetsQuora Question PairsHotpotQANatural QuestionsOK-VQAInfoSeekMVKPolyvorePubMedQAPubMedQA corpus with metadataToolLensคลิปไวรัล!! ไอซ์ ปรีชญา ลืมปิดไลฟ์สดตอนอาบน้ำ ถูกแชร์กระหึ่มเน็ต

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