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 10901–10950 of 14297 papers

TitleStatusHype
ConveRT: Efficient and Accurate Conversational Representations from TransformersCode1
Instance-based Transfer Learning for Multilingual Deep Retrieval—0
Cross-Lingual Relevance Transfer for Document Retrieval—0
How Language-Neutral is Multilingual BERT?Code0
Improved Visual Localization via Graph Smoothing—0
Contextualized Sparse Representations for Real-Time Open-Domain Question AnsweringCode1
Enriching Conversation Context in Retrieval-based Chatbots—0
Self-Attention and Ingredient-Attention Based Model for Recipe Retrieval from Image Queries—0
Fast Optical System Identification by Numerical InterferometryCode0
Deep Heterogeneous Hashing for Face Video Retrieval—0
BERT-CNN: a Hierarchical Patent Classifier Based on a Pre-Trained Language Model—0
Digital phase-only holography using deep conditional generative modelsCode0
MRNN: A Multi-Resolution Neural Network with Duplex Attention for Document Retrieval in the Context of Question Answering—0
Scene Graph based Image Retrieval -- A case study on the CLEVR Dataset—0
GRAPHENE: A Precise Biomedical Literature Retrieval Engine with Graph Augmented Deep Learning and External Knowledge Empowerment—0
Exploring Diverse Expressions for Paraphrase Generation—0
Label Embedding using Hierarchical Structure of Labels for Twitter Classification—0
Bridging the Gap between Relevance Matching and Semantic Matching for Short Text Similarity Modeling—0
A Modular Architecture for Unsupervised Sarcasm Generation—0
Cross-Domain Modeling of Sentence-Level Evidence for Document Retrieval—0
Applying BERT to Document Retrieval with Birch—0
MICRON: Multigranular Interaction for Contextualizing RepresentatiON in Non-factoid Question Answering—0
Multi-hop Selector Network for Multi-turn Response Selection in Retrieval-based ChatbotsCode0
Investigating Cross-Lingual Alignment Methods for Contextualized Embeddings with Token-Level Evaluation—0
Leveraging Medical Literature for Section Prediction in Electronic Health Records—0
Entity resolution for noisy ASR transcripts—0
Learning a Representation for Cover Song Identification Using Convolutional Neural NetworkCode1
Retrieval-guided Dialogue Response Generation via a Matching-to-Generation Framework—0
Toward a Task of Feedback Comment Generation for Writing Learning—0
The Challenges of Optimizing Machine Translation for Low Resource Cross-Language Information Retrieval—0
Sampling Matters! An Empirical Study of Negative Sampling Strategies for Learning of Matching Models in Retrieval-based Dialogue Systems—0
SEAGLE: A Platform for Comparative Evaluation of Semantic Encoders for Information Retrieval—0
On Efficient Retrieval of Top Similarity Vectors—0
Team DOMLIN: Exploiting Evidence Enhancement for the FEVER Shared Task—0
RDoC Task at BioNLP-OST 2019—0
Zero-Shot Cross-lingual Name Retrieval for Low-Resource Languages—0
TextGraphs 2019 Shared Task on Multi-Hop Inference for Explanation Regeneration—0
Team GPLSI. Approach for automated fact checking—0
Weakly Supervised Attentional Model for Low Resource Ad-hoc Cross-lingual Information Retrieval—0
Book QA: Stories of Challenges and Opportunities—0
Generating Sentential Arguments from Diverse Perspectives on Controversial TopicCode0
Extractive NarrativeQA with Heuristic Pre-Training—0
Variance and Error in One-Step Phase-Retrieval—0
Neural Assistant: Joint Action Prediction, Response Generation, and Latent Knowledge Reasoning—0
Visual Appearance Based Person Retrieval in Unconstrained Environment Videos—0
A neural document language modeling framework for spoken document retrieval—0
Lexical Learning as an Online Optimal Experiment: Building Efficient Search Engines through Human-Machine Collaboration—0
A Richly Annotated Corpus for Different Tasks in Automated Fact-CheckingCode0
Learning Without LossCode0
Scalable Evaluation and Improvement of Document Set Expansion via Neural Positive-Unlabeled LearningCode0
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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