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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 526–550 of 14297 papers

TitleStatusHype
Efficient Remote Sensing with Harmonized Transfer Learning and Modality AlignmentCode2
Enabling Large Language Models to Generate Text with CitationsCode2
AIR-Bench: Automated Heterogeneous Information Retrieval BenchmarkCode2
DynamicRAG: Leveraging Outputs of Large Language Model as Feedback for Dynamic Reranking in Retrieval-Augmented GenerationCode2
EarthLoc: Astronaut Photography Localization by Indexing Earth from SpaceCode2
Language Model Powered Digital Biology with BRADCode2
Duoduo CLIP: Efficient 3D Understanding with Multi-View ImagesCode2
MedCPT: Contrastive Pre-trained Transformers with Large-scale PubMed Search Logs for Zero-shot Biomedical Information RetrievalCode2
Do You Remember? Dense Video Captioning with Cross-Modal Memory RetrievalCode2
BIRB: A Generalization Benchmark for Information Retrieval in BioacousticsCode2
Efficient Inverted Indexes for Approximate Retrieval over Learned Sparse RepresentationsCode2
AiSAQ: All-in-Storage ANNS with Product Quantization for DRAM-free Information RetrievalCode2
Efficient Multi-Vector Dense Retrieval Using Bit VectorsCode2
DRAGIN: Dynamic Retrieval Augmented Generation based on the Information Needs of Large Language ModelsCode2
Retrieval with Learned SimilaritiesCode2
ECG-Chat: A Large ECG-Language Model for Cardiac Disease DiagnosisCode2
Blended RAG: Improving RAG (Retriever-Augmented Generation) Accuracy with Semantic Search and Hybrid Query-Based RetrieversCode2
Discrete Event, Continuous Time RNNsCode2
Disentangling Memory and Reasoning Ability in Large Language ModelsCode2
A Foundation Model for Music InformaticsCode2
Evaluation of Retrieval-Augmented Generation: A SurveyCode2
DISC-FinLLM: A Chinese Financial Large Language Model based on Multiple Experts Fine-tuningCode2
Beyond Text: Optimizing RAG with Multimodal Inputs for Industrial ApplicationsCode2
DISC-LawLLM: Fine-tuning Large Language Models for Intelligent Legal ServicesCode2
Distillation Enhanced Generative RetrievalCode2
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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