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 401–425 of 14297 papers

TitleStatusHype
Enhancing Cache-Augmented Generation (CAG) with Adaptive Contextual Compression for Scalable Knowledge Integration—0
Scaling Context, Not Parameters: Training a Compact 7B Language Model for Efficient Long-Context Processing—0
ALOHA: Empowering Multilingual Agent for University Orientation with Hierarchical Retrieval—0
MedEIR: A Specialized Medical Embedding Model for Enhanced Information Retrieval—0
Pre-training vs. Fine-tuning: A Reproducibility Study on Dense Retrieval Knowledge AcquisitionCode0
KAQG: A Knowledge-Graph-Enhanced RAG for Difficulty-Controlled Question Generation—0
Efficient and Reproducible Biomedical Question Answering using Retrieval Augmented GenerationCode1
SEM: Reinforcement Learning for Search-Efficient Large Language Models—0
Reproducibility, Replicability, and Insights into Visual Document Retrieval with Late InteractionCode0
ReCDAP: Relation-Based Conditional Diffusion with Attention Pooling for Few-Shot Knowledge Graph CompletionCode1
Reinforced Internal-External Knowledge Synergistic Reasoning for Efficient Adaptive Search AgentCode2
OnPrem.LLM: A Privacy-Conscious Document Intelligence ToolkitCode4
GRADA: Graph-based Reranker against Adversarial Documents AttackCode0
Exact Spin Elimination in Ising Hamiltonians and Energy-Based Machine Learning—0
DynamicRAG: Leveraging Outputs of Large Language Model as Feedback for Dynamic Reranking in Retrieval-Augmented GenerationCode2
Towards Requirements Engineering for RAG Systems—0
Incomplete In-context Learning—0
QUPID: Quantified Understanding for Enhanced Performance, Insights, and Decisions in Korean Search Engines—0
A Unified Hierarchical Framework for Fine-grained Cross-view Geo-localization over Large-scale Scenarios—0
Benchmarking Retrieval-Augmented Generation for Chemistry—0
Code Retrieval for MILP Instance Generation—0
TrumorGPT: Graph-Based Retrieval-Augmented Large Language Model for Fact-Checking—0
References Indeed Matter? Reference-Free Preference Optimization for Conversational Query Reformulation—0
OMGM: Orchestrate Multiple Granularities and Modalities for Efficient Multimodal Retrieval—0
MacRAG: Compress, Slice, and Scale-up for Multi-Scale Adaptive Context RAGCode1
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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