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 5576–5600 of 14297 papers

TitleStatusHype
AfriQA: Cross-lingual Open-Retrieval Question Answering for African LanguagesCode1
Autonomous GIS: the next-generation AI-powered GISCode2
A Platform for the Biomedical Application of Large Language ModelsCode1
Unsupervised Dense Retrieval Training with Web AnchorsCode0
Extracting Complex Named Entities in Legal Documents via Weakly Supervised Object Detection—0
Evaluating Embedding APIs for Information Retrieval—0
Synthetic Query Generation for Privacy-Preserving Deep Retrieval Systems using Differentially Private Language Models—0
Boosting Zero-shot Cross-lingual Retrieval by Training on Artificially Code-Switched DataCode0
Boosting Visual-Language Models by Exploiting Hard SamplesCode0
Ranking & Reweighting Improves Group Distributional Robustness—0
Region-based Contrastive Pretraining for Medical Image Retrieval with Anatomic Query—0
MAUPQA: Massive Automatically-created Polish Question Answering Dataset—0
Towards Writer Retrieval for Historical DatasetsCode1
Vision-Language Models in Remote Sensing: Current Progress and Future TrendsCode1
Adapt and Align to Improve Zero-Shot Sketch-Based Image Retrieval—0
ImageBind: One Embedding Space To Bind Them AllCode5
VCSUM: A Versatile Chinese Meeting Summarization DatasetCode1
RLocator: Reinforcement Learning for Bug Localization—0
CaseEncoder: A Knowledge-enhanced Pre-trained Model for Legal Case Encoding—0
The Role of Relevance in Fair RankingCode0
Joint Moment Retrieval and Highlight Detection Via Natural Language QueriesCode1
Recommender Systems with Generative RetrievalCode2
Less is More: Removing Text-regions Improves CLIP Training Efficiency and RobustnessCode0
Searching Mobile App Screens via Text + DoodleCode0
ElasticHash: Semantic Image Similarity Search by Deep Hashing with ElasticsearchCode1
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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