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 9701–9725 of 14297 papers

TitleStatusHype
Contrastive Learning for Interactive Recommendation in Fashion—0
Contrastive Learning of 3D Shape Descriptor with Dynamic Adversarial Views—0
Contrastive Learning of Visual-Semantic Embeddings—0
Contrastive Learning to Improve Retrieval for Real-world Fact Checking—0
Contrastive masked auto-encoders based self-supervised hashing for 2D image and 3D point cloud cross-modal retrieval—0
Contrastive Pre-training for Zero-Shot Information Retrieval—0
CoST: Contrastive Quantization based Semantic Tokenization for Generative Recommendation—0
Contrastive Video-Language Learning with Fine-grained Frame Sampling—0
Contrato360 2.0: A Document and Database-Driven Question-Answer System using Large Language Models and Agents—0
ConTReGen: Context-driven Tree-structured Retrieval for Open-domain Long-form Text Generation—0
Contribution au Niveau de l'Approche Indirecte à Base de Transfert dans la Traduction Automatique—0
Contributions to the Improvement of Question Answering Systems in the Biomedical Domain—0
Contri(e)ve: Context + Retrieve for Scholarly Question Answering—0
Control Architecture of the Double-Cross-Correlation Processor for Sampling-Rate-Offset Estimation in Acoustic Sensor Networks—0
Controllable Augmentations for Video Representation Learning—0
Controlled AutoEncoders to Generate Faces from Voices—0
Controlled Retrieval-augmented Context Evaluation for Long-form RAG—0
Controlling Chat Bot Multi-Document Navigation with the Extended Discourse Trees—0
I3: Intent-Introspective Retrieval Conditioned on Instructions—0
Control Search Rankings, Control the World: What is a Good Search Engine?—0
Control Token with Dense Passage Retrieval—0
Conv-CoA: Improving Open-domain Question Answering in Large Language Models via Conversational Chain-of-Action—0
Convergence of Learning Dynamics in Information Retrieval Games—0
Conversation AI Dialog for Medicare powered by Finetuning and Retrieval Augmented Generation—0
Conversational Recommendation as Retrieval: A Simple, Strong Baseline—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