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 9751–9800 of 14297 papers

TitleStatusHype
Learning to Rank with Small Set of Ground Truth Data—0
Learning to Recognise Words using Visually Grounded Speech—0
Learning to Reformulate the Queries on the WEB—0
Learning to Represent Image and Text with Denotation Graph—0
Learning to Retrieve for Job Matching—0
Learning to Retrieve Iteratively for In-Context Learning—0
Learning to retrieve out-of-vocabulary words in speech recognition—0
Learning to Scale Multilingual Representations for Vision-Language Tasks—0
Learning to Select In-Context Demonstration Preferred by Large Language Model—0
Learning to Selectively Learn for Weakly-supervised Paraphrase Generation—0
Learning to Selectively Transfer: Reinforced Transfer Learning for Deep Text Matching—0
Learning to Sketch Human Facial Portraits using Personal Styles by Case-Based Reasoning—0
Learning to Sketch with Shortcut Cycle Consistency—0
Learning to Synthesize: Robust Phase Retrieval at Low Photon counts—0
Learning to synthesize: splitting and recombining low and high spatial frequencies for image recovery—0
Learning to Translate: A Query-Specific Combination Approach for Cross-Lingual Information Retrieval—0
Learning to Truncate Ranked Lists for Information Retrieval—0
Learning to Weight Translations using Ordinal Linear Regression and Query-generated Training Data for Ad-hoc Retrieval with Long Queries—0
Learning Trajectory-Word Alignments for Video-Language Tasks—0
Learning Translational and Knowledge-based Similarities from Relevance Rankings for Cross-Language Retrieval—0
Learning Translation Models from Monolingual Continuous Representations—0
Learning Image Representations for Content Based Image Retrieval of Radiotherapy Treatment Plans—0
Learning Unified Embedding for Apparel Recognition—0
Learning Universal User Representations Leveraging Cross-domain User Intent at Snapchat—0
Learning User Representations for Open Vocabulary Image Hashtag Prediction—0
Learning Visual Features from Snapshots for Web Search—0
Learning Visual Hierarchies with Hyperbolic Embeddings—0
Learning Visually Grounded Sentence Representations—0
Learning Visual N-Grams from Web Data—0
Learning Visual-Semantic Embeddings for Reporting Abnormal Findings on Chest X-rays—0
Learning When to Retrieve, What to Rewrite, and How to Respond in Conversational QA—0
Learning with Batch-wise Optimal Transport Loss for 3D Shape Recognition—0
Learning with Label Noise for Image Retrieval by Selecting Interactions—0
Learning with Multi-modal Gradient Attention for Explainable Composed Image Retrieval—0
Learning with Noisy Correspondence—0
Learning with Noisy Triplet Correspondence for Composed Image Retrieval—0
Learning without the Phase: Regularized PhaseMax Achieves Optimal Sample Complexity—0
Learning World Models for Interactive Video Generation—0
LearnLens: LLM-Enabled Personalised, Curriculum-Grounded Feedback with Educators in the Loop—0
Learnt quasi-transitive similarity for retrieval from large collections of faces—0
Embedding-Informed Adaptive Retrieval-Augmented Generation of Large Language Models—0
Least square ellipsoid fitting using iterative orthogonal transformations—0
LeCoPCR: Legal Concept-guided Prior Case Retrieval for European Court of Human Rights cases—0
Lecture Notes on Neural Information Retrieval—0
Lecture Presentations Multimodal Dataset: Towards Understanding Multimodality in Educational Videos—0
Legal document retrieval across languages: topic hierarchies based on synsets—0
Legal Document Retrieval using Document Vector Embeddings and Deep Learning—0
Legal Element-oriented Modeling with Multi-view Contrastive Learning for Legal Case Retrieval—0
Legal Question Answering using Ranking SVM and Deep Convolutional Neural Network—0
LegalRAG: A Hybrid RAG System for Multilingual Legal Information 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