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 8026–8050 of 14297 papers

TitleStatusHype
Aggregating Binary Local Descriptors for Image Retrieval—0
Document Network Embedding: Coping for Missing Content and Missing Links—0
Metadata Enrichment of Multi-Disciplinary Digital Library: A Semantic-based Approach—0
M6-Rec: Generative Pretrained Language Models are Open-Ended Recommender Systems—0
Document Network Projection in Pretrained Word Embedding Space—0
Meta-Inductive Node Classification across Graphs—0
MessIRve: A Large-Scale Spanish Information Retrieval Dataset—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
Document Retrieval for Large Scale Content Analysis using Contextualized Dictionaries—0
Machine Learning Information Retrieval and Summarisation to Support Systematic Review on Outcomes Based Contracting—0
ChipNeMo: Domain-Adapted LLMs for Chip Design—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
Documents as multiple overlapping windows into grids of counts—0
Adversarial Databases Improve Success in Retrieval-based Large Language Models—0
Machine Translation and Automated Analysis of the Sumerian Language—0
Machine Translation Impact in E-commerce Multilingual Search—0
Evaluating and Enhancing Large Language Models Performance in Domain-specific Medicine: Osteoarthritis Management with DocOA—0
Are Neural Ranking Models Robust?—0
Document Translation vs. Query Translation for Cross-Lingual Information Retrieval in the Medical Domain—0
MetaAID 2.0: An Extensible Framework for Developing Metaverse Applications via Human-controllable Pre-trained Models—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