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 6501–6525 of 14297 papers

TitleStatusHype
Improving Zero-shot LLM Re-Ranker with Risk Minimization—0
IM-RAG: Multi-Round Retrieval-Augmented Generation Through Learning Inner Monologues—0
Improving Bilingual Terminology Extraction from Comparable Corpora via Multiple Word-Space Models—0
Improving BERT-based Query-by-Document Retrieval with Multi-Task Optimization—0
Cross-Modal Manifold Learning for Cross-modal Retrieval—0
Auto-GDA: Automatic Domain Adaptation for Efficient Grounding Verification in Retrieval Augmented Generation—0
Improving average ranking precision in user searches for biomedical research datasets—0
Incomplete In-context Learning—0
In-context denoising with one-layer transformers: connections between attention and associative memory retrieval—0
Exploring Train and Test-Time Augmentations for Audio-Language Learning—0
Improving Attributed Text Generation of Large Language Models via Preference Learning—0
In-Context Learning for Text Classification with Many Labels—0
Cross-Modal Learning via Pairwise Constraints—0
In-Context Learning with Long-Context Models: An In-Depth Exploration—0
AutoFAS: Automatic Feature and Architecture Selection for Pre-Ranking System—0
In-Context Prompt Editing For Conditional Audio Generation—0
AMUSED: A Multi-Stream Vector Representation Method for Use in Natural Dialogue—0
Adaptive Substring Extraction and Modified Local NBNN Scoring for Binary Feature-based Local Mobile Visual Search without False Positives—0
Improving Assessment of Tutoring Practices using Retrieval-Augmented Generation—0
Improving Acquisition Speed of X-Ray Ptychography through Spatial Undersampling and Regularization—0
Incorporating Intra-Class Variance to Fine-Grained Visual Recognition—0
Improving accuracy of GPT-3/4 results on biomedical data using a retrieval-augmented language model—0
Improving Accountability in Recommender Systems Research Through Reproducibility—0
Incorporating Multimodal Information in Open-Domain Web Keyphrase Extraction—0
Cross-Modality Personalization for 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