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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 10026–10050 of 14297 papers

TitleStatusHype
M6-Rec: Generative Pretrained Language Models are Open-Ended Recommender Systems—0
Machine Against the RAG: Jamming Retrieval-Augmented Generation with Blocker Documents—0
Machine Learning Approaches for Amharic Parts-of-speech Tagging—0
Machine learning approach for text and document mining—0
Machine Learning, Clustering, and Polymorphy—0
Machine Learning for Food Review and Recommendation—0
Machine Learning Information Retrieval and Summarisation to Support Systematic Review on Outcomes Based Contracting—0
Machine learning methods for multimedia information retrieval—0
Machine Learning with World Knowledge: The Position and Survey—0
Machine Psychology—0
Machine Reading Comprehension for Answer Re-Ranking in Customer Support Chatbots—0
Machine Translation and Automated Analysis of the Sumerian Language—0
Machine Translation Impact in E-commerce Multilingual Search—0
Machining feature recognition using descriptors with range constraints for mechanical 3D models—0
LLM-Consensus: Multi-Agent Debate for Visual Misinformation Detection—0
Mafin: Enhancing Black-Box Embeddings with Model Augmented Fine-Tuning—0
Magic Mushroom: A Customizable Benchmark for Fine-grained Analysis of Retrieval Noise Erosion in RAG Systems—0
MAGMaR Shared Task System Description: Video Retrieval with OmniEmbed—0
Magnushammer: A Transformer-Based Approach to Premise Selection—0
Maia: A Real-time Non-Verbal Chat for Human-AI Interaction—0
MAIN-RAG: Multi-Agent Filtering Retrieval-Augmented Generation—0
MAKE: Vision-Language Pre-training based Product Retrieval in Taobao Search—0
Making Better Use of Edges via Perceptual Grouping—0
Making Information Seeking Easier: An Improved Pipeline for Conversational Search—0
Making Large Language Models A Better Foundation For Dense 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