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 551600 of 14297 papers

TitleStatusHype
Empowering Large Language Models to Set up a Knowledge Retrieval Indexer via Self-LearningCode2
Enabling Large Language Models to Generate Text with CitationsCode2
Egocentric Video-Language PretrainingCode2
Retrieval with Learned SimilaritiesCode2
Egocentric Video-Language Pretraining @ EPIC-KITCHENS-100 Multi-Instance Retrieval Challenge 2022Code2
Generating Benchmarks for Factuality Evaluation of Language ModelsCode2
Explore the Limits of Omni-modal Pretraining at ScaleCode2
Associative Recurrent Memory TransformerCode2
Fine-grained Image Captioning with CLIP RewardCode2
Efficiently Learning at Test-Time: Active Fine-Tuning of LLMsCode2
A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open QuestionsCode2
A Survey of Personalization: From RAG to AgentCode2
Efficient Inverted Indexes for Approximate Retrieval over Learned Sparse RepresentationsCode2
EarthLoc: Astronaut Photography Localization by Indexing Earth from SpaceCode2
ECG-Chat: A Large ECG-Language Model for Cardiac Disease DiagnosisCode2
Efficient Multi-Vector Dense Retrieval Using Bit VectorsCode2
DRAGIN: Dynamic Retrieval Augmented Generation based on the Information Needs of Large Language ModelsCode2
Duoduo CLIP: Efficient 3D Understanding with Multi-View ImagesCode2
Document Expansion by Query PredictionCode2
Do You Remember? Dense Video Captioning with Cross-Modal Memory RetrievalCode2
DynamicRAG: Leveraging Outputs of Large Language Model as Feedback for Dynamic Reranking in Retrieval-Augmented GenerationCode2
EfficientRAG: Efficient Retriever for Multi-Hop Question AnsweringCode2
Discrete Event, Continuous Time RNNsCode2
AudioSetCaps: An Enriched Audio-Caption Dataset using Automated Generation Pipeline with Large Audio and Language ModelsCode2
DISC-LawLLM: Fine-tuning Large Language Models for Intelligent Legal ServicesCode2
Disentangling Memory and Reasoning Ability in Large Language ModelsCode2
Diffusion Posterior Sampling for General Noisy Inverse ProblemsCode2
DISC-FinLLM: A Chinese Financial Large Language Model based on Multiple Experts Fine-tuningCode2
Improving Medical Reasoning through Retrieval and Self-Reflection with Retrieval-Augmented Large Language ModelsCode2
Automated Evaluation of Retrieval-Augmented Language Models with Task-Specific Exam GenerationCode2
In-Context Retrieval-Augmented Language ModelsCode2
InPars: Data Augmentation for Information Retrieval using Large Language ModelsCode2
Distillation Enhanced Generative RetrievalCode2
All You Need to Know About Training Image Retrieval ModelsCode2
Detect-Order-Construct: A Tree Construction based Approach for Hierarchical Document Structure AnalysisCode2
Autoregressive Search Engines: Generating Substrings as Document IdentifiersCode2
Interactive Continual Learning: Fast and Slow ThinkingCode2
Demystifying and Enhancing the Efficiency of Large Language Model Based Search AgentsCode2
Dense Text Retrieval based on Pretrained Language Models: A SurveyCode2
Investigating the Role of Image Retrieval for Visual Localization -- An exhaustive benchmarkCode2
DiffCLIP: Differential Attention Meets CLIPCode2
Efficient Remote Sensing with Harmonized Transfer Learning and Modality AlignmentCode2
BEBLID: Boosted efficient binary local image descriptorCode2
BEIR: A Heterogenous Benchmark for Zero-shot Evaluation of Information Retrieval ModelsCode2
Fine-grained Late-interaction Multi-modal Retrieval for Retrieval Augmented Visual Question AnsweringCode2
LLM-based SPARQL Query Generation from Natural Language over Federated Knowledge GraphsCode2
Deep Hashing Network for Unsupervised Domain AdaptationCode1
Deep Evidential Learning with Noisy Correspondence for Cross-Modal RetrievalCode1
Deep metric learning using Triplet networkCode1
Deeper Convolutional Neural Networks and Broad Augmentation Policies Improve Performance in Musical Key EstimationCode1
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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