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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 551–575 of 14297 papers

TitleStatusHype
ECG-Chat: A Large ECG-Language Model for Cardiac Disease DiagnosisCode2
FollowIR: Evaluating and Teaching Information Retrieval Models to Follow InstructionsCode2
Efficient Inverted Indexes for Approximate Retrieval over Learned Sparse RepresentationsCode2
AriGraph: Learning Knowledge Graph World Models with Episodic Memory for LLM AgentsCode2
EfficientRAG: Efficient Retriever for Multi-Hop Question AnsweringCode2
Enabling Large Language Models to Generate Text with CitationsCode2
FaithEval: Can Your Language Model Stay Faithful to Context, Even If "The Moon is Made of Marshmallows"Code2
DRAGIN: Dynamic Retrieval Augmented Generation based on the Information Needs of Large Language ModelsCode2
Associative Recurrent Memory TransformerCode2
A Survey of Large Language Model Empowered Agents for Recommendation and Search: Towards Next-Generation Information RetrievalCode2
Do You Remember? Dense Video Captioning with Cross-Modal Memory RetrievalCode2
Document Expansion by Query PredictionCode2
Duoduo CLIP: Efficient 3D Understanding with Multi-View ImagesCode2
Discrete Event, Continuous Time RNNsCode2
A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open QuestionsCode2
A Survey of Personalization: From RAG to AgentCode2
DISC-LawLLM: Fine-tuning Large Language Models for Intelligent Legal ServicesCode2
Disentangling Memory and Reasoning Ability in Large Language ModelsCode2
DISC-FinLLM: A Chinese Financial Large Language Model based on Multiple Experts Fine-tuningCode2
Distillation Enhanced Generative RetrievalCode2
DynamicRAG: Leveraging Outputs of Large Language Model as Feedback for Dynamic Reranking in Retrieval-Augmented GenerationCode2
Dense Text Retrieval based on Pretrained Language Models: A SurveyCode2
Demystifying and Enhancing the Efficiency of Large Language Model Based Search AgentsCode2
Detect-Order-Construct: A Tree Construction based Approach for Hierarchical Document Structure AnalysisCode2
All You Need to Know About Training Image Retrieval ModelsCode2
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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