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

Showing 551575 of 14297 papers

TitleStatusHype
ECG-Chat: A Large ECG-Language Model for Cardiac Disease DiagnosisCode2
AIR-Bench: Automated Heterogeneous Information Retrieval BenchmarkCode2
Efficient Inverted Indexes for Approximate Retrieval over Learned Sparse RepresentationsCode2
FLAIR: VLM with Fine-grained Language-informed Image RepresentationsCode2
Efficient Remote Sensing with Harmonized Transfer Learning and Modality AlignmentCode2
FollowIR: Evaluating and Teaching Information Retrieval Models to Follow InstructionsCode2
Exploring the best way for UAV visual localization under Low-altitude Multi-view Observation Condition: a BenchmarkCode2
DRAGIN: Dynamic Retrieval Augmented Generation based on the Information Needs of Large Language ModelsCode2
Do You Remember? Dense Video Captioning with Cross-Modal Memory RetrievalCode2
Language Model Powered Digital Biology with BRADCode2
BIRB: A Generalization Benchmark for Information Retrieval in BioacousticsCode2
BrowseComp-ZH: Benchmarking Web Browsing Ability of Large Language Models in ChineseCode2
Duoduo CLIP: Efficient 3D Understanding with Multi-View ImagesCode2
Distillation Enhanced Generative RetrievalCode2
GiantMIDI-Piano: A large-scale MIDI dataset for classical piano musicCode2
GLAP: General contrastive audio-text pretraining across domains and languagesCode2
MedCPT: Contrastive Pre-trained Transformers with Large-scale PubMed Search Logs for Zero-shot Biomedical Information RetrievalCode2
Discrete Event, Continuous Time RNNsCode2
Disentangling Memory and Reasoning Ability in Large Language ModelsCode2
Document Expansion by Query PredictionCode2
DynamicRAG: Leveraging Outputs of Large Language Model as Feedback for Dynamic Reranking in Retrieval-Augmented GenerationCode2
Diffusion Posterior Sampling for General Noisy Inverse ProblemsCode2
Beyond Text: Optimizing RAG with Multimodal Inputs for Industrial ApplicationsCode2
Detect-Order-Construct: A Tree Construction based Approach for Hierarchical Document Structure AnalysisCode2
DiffCLIP: Differential Attention Meets CLIPCode2
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1BM25SQueries per second183.53Unverified
2ElasticsearchQueries per second21.8Unverified
3BM25-PTQueries per second6.49Unverified
4Rank-BM25Queries per second1.18Unverified
#ModelMetricClaimedVerifiedStatus
1BM25SQueries per second20.88Unverified
2ElasticsearchQueries per second7.11Unverified
3Rank-BM25Queries per second0.04Unverified
#ModelMetricClaimedVerifiedStatus
1BM25SQueries per second41.85Unverified
2ElasticsearchQueries per second12.16Unverified
3Rank-BM25Queries per second0.1Unverified
#ModelMetricClaimedVerifiedStatus
1FLMRRecall@589.32Unverified
2RA-VQARecall@582.84Unverified
#ModelMetricClaimedVerifiedStatus
1PreFLMRRecall@562.1Unverified
#ModelMetricClaimedVerifiedStatus
1CLIP-KIStext-to-video Mean Rank30Unverified
#ModelMetricClaimedVerifiedStatus
1CLIP4OutfitRecall@57.59Unverified
#ModelMetricClaimedVerifiedStatus
1MetaGen Blended RAGAccuracy (Top-1)82.1Unverified
#ModelMetricClaimedVerifiedStatus
1MetaGen Blended RAGAccuracy (Top-1)82.1Unverified
#ModelMetricClaimedVerifiedStatus
1COLTCOMP@84.55Unverified
#ModelMetricClaimedVerifiedStatus
1hello0L1,121,222Unverified