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 501–550 of 14297 papers

TitleStatusHype
The All-Seeing Project: Towards Panoptic Visual Recognition and Understanding of the Open WorldCode2
BEIR: A Heterogenous Benchmark for Zero-shot Evaluation of Information Retrieval ModelsCode2
Benchmarking Large Language Models in Retrieval-Augmented GenerationCode2
Benchmarking Retrieval-Augmented Generation in Multi-Modal ContextsCode2
All-In-One Metrical And Functional Structure Analysis With Neighborhood Attentions on Demixed AudioCode2
Contrastive language and vision learning of general fashion conceptsCode2
TMR: Text-to-Motion Retrieval Using Contrastive 3D Human Motion SynthesisCode2
All in One: Exploring Unified Video-Language Pre-trainingCode2
Toward General Instruction-Following Alignment for Retrieval-Augmented GenerationCode2
All You Need to Know About Training Image Retrieval ModelsCode2
FaithEval: Can Your Language Model Stay Faithful to Context, Even If "The Moon is Made of Marshmallows"Code2
Fast Think-on-Graph: Wider, Deeper and Faster Reasoning of Large Language Model on Knowledge GraphCode2
Extended Mind TransformersCode2
Exploring the best way for UAV visual localization under Low-altitude Multi-view Observation Condition: a BenchmarkCode2
Explore the Limits of Omni-modal Pretraining at ScaleCode2
EraRAG: Efficient and Incremental Retrieval Augmented Generation for Growing CorporaCode2
Evaluating RAG-Fusion with RAGElo: an Automated Elo-based FrameworkCode2
Exploring a Fine-Grained Multiscale Method for Cross-Modal Remote Sensing Image RetrievalCode2
FActScore: Fine-grained Atomic Evaluation of Factual Precision in Long Form Text GenerationCode2
FedRAG: A Framework for Fine-Tuning Retrieval-Augmented Generation SystemsCode2
Egocentric Video-Language Pretraining @ EPIC-KITCHENS-100 Multi-Instance Retrieval Challenge 2022Code2
Egocentric Video-Language PretrainingCode2
Empowering Large Language Models to Set up a Knowledge Retrieval Indexer via Self-LearningCode2
EfficientRAG: Efficient Retriever for Multi-Hop Question AnsweringCode2
Efficiently Learning at Test-Time: Active Fine-Tuning of LLMsCode2
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