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 11001–11025 of 14297 papers

TitleStatusHype
Exploring EEG for Object Detection and Retrieval—0
Exploring Federated Self-Supervised Learning for General Purpose Audio Understanding—0
Exploring Fine-tuning Techniques for Pre-trained Cross-lingual Models via Continual Learning—0
Debiasing Gender Bias in Information Retrieval Models—0
Exploring Inherent Properties of the Monophonic Melody of Songs—0
Exploring Knowledge Boundaries in Large Language Models for Retrieval Judgment—0
Exploring LLM-based Agents for Root Cause Analysis—0
Exploring Low-light Object Detection Techniques—0
Exploring new Approaches for Information Retrieval through Natural Language Processing—0
Exploring Optimal Transport-Based Multi-Grained Alignments for Text-Molecule Retrieval—0
Exploring Query Categorisation for Query Expansion: A Study—0
Exploring Relations in Untrimmed Videos for Self-Supervised Learning—0
Exploring the Capabilities and Limitations of Large Language Models in the Electric Energy Sector—0
Open Domain Multi-document Summarization: A Comprehensive Study of Model Brittleness under Retrieval—0
Exploring the context of visual information seeking—0
Exploring the Distinctiveness and Fidelity of the Descriptions Generated by Large Vision-Language Models—0
Exploring the Effectiveness of Convolutional Neural Networks for Answer Selection in End-to-End Question Answering—0
Exploring the Ideal Depth of Neural Network when Predicting Question Deletion on Community Question Answering—0
Exploring the In-context Learning Ability of Large Language Model for Biomedical Concept Linking—0
Modeling Uncertainty and Using Post-fusion as Fallback Improves Retrieval Augmented Generation with LLMs—0
Exploring the landscape of large language models: Foundations, techniques, and challenges—0
Exploring the Nexus Between Retrievability and Query Generation Strategies—0
Exploring the Nexus of Large Language Models and Legal Systems: A Short Survey—0
Exploring the Value of Multi-View Learning for Session-Aware Query Representation—0
Exploring the Value of Multi-View Learning for Session-Aware Query Representation—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