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 76100 of 14297 papers

TitleStatusHype
AdaVideoRAG: Omni-Contextual Adaptive Retrieval-Augmented Efficient Long Video UnderstandingCode0
Tree-Based Text Retrieval via Hierarchical Clustering in RAGFrameworks: Application on Taiwanese RegulationsCode0
Hierarchical Multi-Positive Contrastive Learning for Patent Image Retrieval0
A Semantically-Aware Relevance Measure for Content-Based Medical Image Retrieval Evaluation0
Are manual annotations necessary for statutory interpretations retrieval?0
What Matters in Learning from Large-Scale Datasets for Robot Manipulation0
SimpleDoc: Multi-Modal Document Understanding with Dual-Cue Page Retrieval and Iterative RefinementCode1
DeSPITE: Exploring Contrastive Deep Skeleton-Pointcloud-IMU-Text Embeddings for Advanced Point Cloud Human Activity Understanding0
eLog analysis for accelerators: status and future outlook0
SciSage: A Multi-Agent Framework for High-Quality Scientific Survey Generation0
MM-R5: MultiModal Reasoning-Enhanced ReRanker via Reinforcement Learning for Document RetrievalCode0
CORONA: A Coarse-to-Fine Framework for Graph-based Recommendation with Large Language Models0
FlexRAG: A Flexible and Comprehensive Framework for Retrieval-Augmented GenerationCode3
RAG+: Enhancing Retrieval-Augmented Generation with Application-Aware Reasoning0
DeepResearch Bench: A Comprehensive Benchmark for Deep Research AgentsCode4
Chunk Twice, Embed Once: A Systematic Study of Segmentation and Representation Trade-offs in Chemistry-Aware Retrieval-Augmented Generation0
EgoPrivacy: What Your First-Person Camera Says About You?Code0
Bias Amplification in RAG: Poisoning Knowledge Retrieval to Steer LLMs0
GLAP: General contrastive audio-text pretraining across domains and languagesCode2
Reasoning RAG via System 1 or System 2: A Survey on Reasoning Agentic Retrieval-Augmented Generation for Industry ChallengesCode0
ContextRefine-CLIP for EPIC-KITCHENS-100 Multi-Instance Retrieval Challenge 2025Code0
Constructing and Evaluating Declarative RAG Pipelines in PyTerrierCode1
On the role of non-linear latent features in bipartite generative neural networks0
MSTAR: Box-free Multi-query Scene Text Retrieval with Attention RecyclingCode0
TableRAG: A Retrieval Augmented Generation Framework for Heterogeneous Document ReasoningCode2
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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