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 8851–8875 of 14297 papers

TitleStatusHype
Complexity-Based Prompting for Multi-Step Reasoning—0
A Statistical Model of Riemannian Metric Variation for Deformable Shape Analysis—0
Flexible Prefrontal Control over Hippocampal Episodic Memory for Goal-Directed Generalization—0
FLEX-CLIP: Feature-Level GEneration Network Enhanced CLIP for X-shot Cross-modal Retrieval—0
Complex Dynamics in Simple Neural Networks: Understanding Gradient Flow in Phase Retrieval—0
A Statistical Model for Melody Reduction—0
FlashRAG: A Modular Toolkit for Efficient Retrieval-Augmented Generation Research—0
Completion of Missing Labels for Multi-Label Annotation by a Unified Graph Laplacian Regularization—0
FLASH: Federated Learning-Based LLMs for Advanced Query Processing in Social Networks through RAG—0
FlashCheck: Exploration of Efficient Evidence Retrieval for Fast Fact-Checking—0
A Statistical Framework for Data-dependent Retrieval-Augmented Models—0
FlashBack:Efficient Retrieval-Augmented Language Modeling for Long Context Inference—0
FLAP: Fast Language-Audio Pre-training—0
Complete Cross-triplet Loss in Label Space for Audio-visual Cross-modal Retrieval—0
FLAME: A small language model for spreadsheet formulas—0
A Static Pruning Study on Sparse Neural Retrievers—0
A Large Language Model-based Framework for Semi-Structured Tender Document Retrieval-Augmented Generation—0
Active Learning of Ordinal Embeddings: A User Study on Football Data—0
FJWU Participation for the WMT21 Biomedical Translation Task—0
Complementing Lexical Retrieval with Semantic Residual Embedding—0
Fixing Data That Hurts Performance: Cascading LLMs to Relabel Hard Negatives for Robust Information Retrieval—0
Complementarity, F-score, and NLP Evaluation—0
FIT-RAG: Black-Box RAG with Factual Information and Token Reduction—0
Competitive Retrieval: Going Beyond the Single Query—0
FiSTECH: Financial Style Transfer to Enhance Creativity without Hallucinations in LLMs—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