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 11101–11150 of 14297 papers

TitleStatusHype
VECO: Variable Encoder-decoder Pre-training for Cross-lingual Understanding and Generation—0
Quality Over Quantity? LLM-Based Curation for a Data-Efficient Audio-Video Foundation Model—0
RetrievalGuard: Provably Robust 1-Nearest Neighbor Image Retrieval—0
Retrieval-guided Counterfactual Generation for QA—0
Retrieval-guided Counterfactual Generation for QA—0
Retrieval-guided Cross-view Image Synthesis—0
Retrieval-guided Dialogue Response Generation via a Matching-to-Generation Framework—0
Retrieval-Guided Reinforcement Learning for Boolean Circuit Minimization—0
Quality and Cost Trade-offs in Passage Re-ranking Task—0
Large Language Models Leverage External Knowledge to Extend Clinical Insight Beyond Language Boundaries—0
VECO: Variable and Flexible Cross-lingual Pre-training for Language Understanding and Generation—0
Retrieval in Long Surveillance Videos using User Described Motion and Object Attributes—0
Retrieval Instead of Fine-tuning: A Retrieval-based Parameter Ensemble for Zero-shot Learning—0
QualiAssistant: Extracting Qualia Structures from Texts—0
QuadrupletBERT: An Efficient Model For Embedding-Based Large-Scale Retrieval—0
Retrieval meets Long Context Large Language Models—0
Retrieval Meets Reasoning: Dynamic In-Context Editing for Long-Text Understanding—0
Retrieval Meets Reasoning: Even High-school Textbook Knowledge Benefits Multimodal Reasoning—0
Retrieval of aboveground crop nitrogen content with a hybrid machine learning method—0
Retrieval of Boost Invariant Symbolic Observables via Feature Importance—0
On Using Non-Volatile Memory in Apache Lucene—0
Retrieval of Coloured Dissolved Organic Matter with Machine Learning Methods—0
Retrieval of Experiments by Efficient Estimation of Marginal Likelihood—0
Retrieval of Experiments with Sequential Dirichlet Process Mixtures in Model Space—0
Vector Representations of Idioms in Conversational Systems—0
Retrieval of multimedia stimuli with semantic and emotional cues: Suggestions from a controlled study—0
Retrieval of Research-level Mathematical Information Needs: A Test Collection and Technical Terminology Experiment—0
Retrieval of Scientific and Technological Resources for Experts and Scholars—0
QUADRo: Dataset and Models for QUestion-Answer Database Retrieval—0
Retrieval of sun-induced plant fluorescence in the O_2-A absorption band from DESIS imagery—0
Retrieval of surgical phase transitions using reinforcement learning—0
Retrieval of the Best Counterargument without Prior Topic Knowledge—0
Vector representations of text data in deep learning—0
Retrieval Or Holistic Understanding? Dolce: Differentiate Our Long Context Evaluation Tasks—0
QSAM-Net: Rain streak removal by quaternion neural network with self-attention module—0
QontSum: On Contrasting Salient Content for Query-focused Summarization—0
QOG:Question and Options Generation based on Language Model—0
Retrieval-Reasoning Large Language Model-based Synthetic Clinical Trial Generation—0
Retrieval, Reasoning, Re-ranking: A Context-Enriched Framework for Knowledge Graph Completion—0
PAR: Prompt-Aware Token Reduction Method for Efficient Large Multimodal Models—0
Retrieval, Re-ranking and Multi-task Learning for Knowledge-Base Question Answering—0
Q-Map: Clinical Concept Mining from Clinical Documents—0
QE-RAG: A Robust Retrieval-Augmented Generation Benchmark for Query Entry Errors—0
Retrievals Can Be Detrimental: A Contrastive Backdoor Attack Paradigm on Retrieval-Augmented Diffusion Models—0
QEQR: An Exploration of Query Expansion Methods for Question Retrieval in CQA Services—0
RetrievalSum: A Retrieval Enhanced Framework for Abstractive Summarization—0
Retrieval Term Prediction Using Deep Belief Networks—0
Retrieval Term Prediction Using Deep Learning Methods—0
VectorSearch: Enhancing Document Retrieval with Semantic Embeddings and Optimized Search—0
Retrieve-and-Fill for Scenario-based Task-Oriented Semantic Parsing—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