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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 1701–1725 of 14297 papers

TitleStatusHype
Robust Bayesian Scene Reconstruction by Leveraging Retrieval-Augmented Priors—0
Retrieval-guided Cross-view Image Synthesis—0
Zero-Indexing Internet Search Augmented Generation for Large Language Models—0
Advanced System Integration: Analyzing OpenAPI Chunking for Retrieval-Augmented Generation—0
Towards Understanding Retrieval Accuracy and Prompt Quality in RAG Systems—0
CAREL: Instruction-guided reinforcement learning with cross-modal auxiliary objectivesCode0
Zero-shot Musical Stem Retrieval with Joint-Embedding Predictive Architectures—0
SIMS: Simulating Stylized Human-Scene Interactions with Retrieval-Augmented Script Generation—0
RAGDiffusion: Faithful Cloth Generation via External Knowledge Assimilation—0
Multimodal Whole Slide Foundation Model for PathologyCode4
EFSA: Episodic Few-Shot Adaptation for Text-to-Image Retrieval—0
Efficient Learning Content Retrieval with Knowledge Injection—0
UrbanCAD: Towards Highly Controllable and Photorealistic 3D Vehicles for Urban Scene Simulation—0
ICLERB: In-Context Learning Embedding and Reranker Benchmark—0
AudioSetCaps: An Enriched Audio-Caption Dataset using Automated Generation Pipeline with Large Audio and Language ModelsCode2
Deep Plug-and-Play HIO Approach for Phase Retrieval—0
USTCCTSU at SemEval-2024 Task 1: Reducing Anisotropy for Cross-lingual Semantic Textual Relatedness Task—0
RevPRAG: Revealing Poisoning Attacks in Retrieval-Augmented Generation through LLM Activation Analysis—0
Habit Coach: Customising RAG-based chatbots to support behavior change—0
SentiXRL: An advanced large language Model Framework for Multilingual Fine-Grained Emotion Classification in Complex Text Environment—0
Unifying Generative and Dense Retrieval for Sequential Recommendation—0
Overview of TREC 2024 Biomedical Generative Retrieval (BioGen) Track—0
Automated Literature Review Using NLP Techniques and LLM-Based Retrieval-Augmented Generation—0
2D Matryoshka Training for Information RetrievalCode0
FLEX-CLIP: Feature-Level GEneration Network Enhanced CLIP for X-shot Cross-modal Retrieval—0
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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