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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 426–450 of 14297 papers

TitleStatusHype
Atlas: Few-shot Learning with Retrieval Augmented Language ModelsCode2
FreshDiskANN: A Fast and Accurate Graph-Based ANN Index for Streaming Similarity SearchCode2
OpenICL: An Open-Source Framework for In-context LearningCode2
EraRAG: Efficient and Incremental Retrieval Augmented Generation for Growing CorporaCode2
Enhancing Retrieval-Augmented Generation: A Study of Best PracticesCode2
Enabling Large Language Models to Generate Text with CitationsCode2
Evaluating RAG-Fusion with RAGElo: an Automated Elo-based FrameworkCode2
Egocentric Video-Language PretrainingCode2
Benchmarking Retrieval-Augmented Generation in Multi-Modal ContextsCode2
Egocentric Video-Language Pretraining @ EPIC-KITCHENS-100 Multi-Instance Retrieval Challenge 2022Code2
PersonaRAG: Enhancing Retrieval-Augmented Generation Systems with User-Centric AgentsCode2
AiSAQ: All-in-Storage ANNS with Product Quantization for DRAM-free Information RetrievalCode2
Retrieval with Learned SimilaritiesCode2
Empowering Large Language Models to Set up a Knowledge Retrieval Indexer via Self-LearningCode2
Evaluation of Retrieval-Augmented Generation: A SurveyCode2
Associative Recurrent Memory TransformerCode2
Position Interpolation Improves ALiBi ExtrapolationCode2
Pretrained Transformers for Text Ranking: BERT and BeyondCode2
Pretrained Visual UncertaintiesCode2
Prior Knowledge Integration via LLM Encoding and Pseudo Event Regulation for Video Moment RetrievalCode2
Benchmarking Large Language Models in Retrieval-Augmented GenerationCode2
Efficient Multi-Vector Dense Retrieval Using Bit VectorsCode2
Pyserini: An Easy-to-Use Python Toolkit to Support Replicable IR Research with Sparse and Dense RepresentationsCode2
A Survey of Personalization: From RAG to AgentCode2
AIR-Bench: Automated Heterogeneous Information Retrieval BenchmarkCode2
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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