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 301–325 of 14297 papers

TitleStatusHype
How Do Large Vision-Language Models See Text in Image? Unveiling the Distinctive Role of OCR Heads—0
Highlighting What Matters: Promptable Embeddings for Attribute-Focused Image Retrieval—0
Diffusion vs. Autoregressive Language Models: A Text Embedding Perspective—0
CRAFT: Training-Free Cascaded Retrieval for Tabular QA—0
Do RAG Systems Suffer From Positional Bias?—0
An Alternative to FLOPS Regularization to Effectively Productionize SPLADE-Doc—0
ConvSearch-R1: Enhancing Query Reformulation for Conversational Search with Reasoning via Reinforcement LearningCode2
The Atlas of In-Context Learning: How Attention Heads Shape In-Context Retrieval AugmentationCode1
Scalable Defense against In-the-wild Jailbreaking Attacks with Safety Context Retrieval—0
ChartCards: A Chart-Metadata Generation Framework for Multi-Task Chart UnderstandingCode0
Single LLM, Multiple Roles: A Unified Retrieval-Augmented Generation Framework Using Role-Specific Token Optimization—0
MIRB: Mathematical Information Retrieval BenchmarkCode0
LiveVLM: Efficient Online Video Understanding via Streaming-Oriented KV Cache and Retrieval—0
HDLxGraph: Bridging Large Language Models and HDL Repositories via HDL Graph DatabasesCode0
Reinforcing Question Answering Agents with Minimalist Policy Gradient Optimization—0
DisastIR: A Comprehensive Information Retrieval Benchmark for Disaster ManagementCode1
SCAN: Semantic Document Layout Analysis for Textual and Visual Retrieval-Augmented Generation—0
Knowledge Graph Based Repository-Level Code Generation—0
Cross-Domain Diffusion with Progressive Alignment for Efficient Adaptive Retrieval—0
Multimodal RAG-driven Anomaly Detection and Classification in Laser Powder Bed Fusion using Large Language Models—0
Beginning with You: Perceptual-Initialization Improves Vision-Language Representation and Alignment—0
Process vs. Outcome Reward: Which is Better for Agentic RAG Reinforcement LearningCode1
Automatic Dataset Generation for Knowledge Intensive Question Answering Tasks—0
Benchmarking the Myopic Trap: Positional Bias in Information RetrievalCode5
s3: You Don't Need That Much Data to Train a Search Agent via RLCode4
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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