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

Showing 15511600 of 14297 papers

TitleStatusHype
CDF-RAG: Causal Dynamic Feedback for Adaptive Retrieval-Augmented GenerationCode1
CCL: Continual Contrastive Learning for LiDAR Place RecognitionCode1
A Multi-Granularity-Aware Aspect Learning Model for Multi-Aspect Dense RetrievalCode1
Augmenting Black-box LLMs with Medical Textbooks for Biomedical Question Answering (Published in Findings of EMNLP 2024)Code1
Aggretriever: A Simple Approach to Aggregate Textual Representations for Robust Dense Passage RetrievalCode1
DeepPatent: Large scale patent drawing recognition and retrievalCode1
Augmenting Document Representations for Dense Retrieval with Interpolation and PerturbationCode1
Learning Diverse Document Representations with Deep Query Interactions for Dense RetrievalCode1
Deep Relational Metric LearningCode1
Deep Reinforcement Learning with Task-Adaptive Retrieval via HypernetworkCode1
From RAG to QA-RAG: Integrating Generative AI for Pharmaceutical Regulatory Compliance ProcessCode1
Frozen in Time: A Joint Video and Image Encoder for End-to-End RetrievalCode1
Learning Interpretable Legal Case Retrieval via Knowledge-Guided Case ReformulationCode1
Learning Intra-Batch Connections for Deep Metric LearningCode1
Deep Self-Supervised Representation Learning for Free-Hand SketchCode1
Learning Modal-Invariant and Temporal-Memory for Video-based Visible-Infrared Person Re-IdentificationCode1
Fuzzy Multimodal Learning for Trusted Cross-modal RetrievalCode1
From Little Things Big Things Grow: A Collection with Seed Studies for Medical Systematic Review Literature SearchCode1
Learning Progressive Modality-shared Transformers for Effective Visible-Infrared Person Re-identificationCode1
AugNet: End-to-End Unsupervised Visual Representation Learning with Image AugmentationCode1
AUGNLG: Few-shot Natural Language Generation using Self-trained Data AugmentationCode1
A Unified End-to-End Retriever-Reader Framework for Knowledge-based VQACode1
A Unified Framework for Learned Sparse RetrievalCode1
Learning Sequence Descriptor based on Spatio-Temporal Attention for Visual Place RecognitionCode1
Deep Unsupervised Image Hashing by Maximizing Bit EntropyCode1
Deep Triplet Hashing Network for Case-based Medical Image RetrievalCode1
DeepTrust: A Reliable Financial Knowledge Retrieval Framework For Explaining Extreme Pricing AnomaliesCode1
Learning Super-Features for Image RetrievalCode1
From Association to Generation: Text-only Captioning by Unsupervised Cross-modal MappingCode1
Defending Against Social Engineering Attacks in the Age of LLMsCode1
Democratizing Large Language Models via Personalized Parameter-Efficient Fine-tuningCode1
Delaying Interaction Layers in Transformer-based Encoders for Efficient Open Domain Question AnsweringCode1
Efficient k-NN Search with Cross-Encoders using Adaptive Multi-Round CUR DecompositionCode1
CBR-RAG: Case-Based Reasoning for Retrieval Augmented Generation in LLMs for Legal Question AnsweringCode1
A multi-task semi-supervised framework for Text2Graph & Graph2TextCode1
Learning To Generate Piano Music With Sustain PedalsCode1
Dense-Captioning Events in VideosCode1
From Artificially Real to Real: Leveraging Pseudo Data from Large Language Models for Low-Resource Molecule DiscoveryCode1
Dense Hierarchical Retrieval for Open-Domain Question AnsweringCode1
Auto-Encoding Twin-Bottleneck HashingCode1
ChatGPT-powered Conversational Drug Editing Using Retrieval and Domain FeedbackCode1
Dense Relational Image Captioning via Multi-task Triple-Stream NetworksCode1
DenserNet: Weakly Supervised Visual Localization Using Multi-scale Feature AggregationCode1
AutoKG: Efficient Automated Knowledge Graph Generation for Language ModelsCode1
Densifying Sparse Representations for Passage Retrieval by Representational SlicingCode1
Dense X Retrieval: What Retrieval Granularity Should We Use?Code1
Learning to SampleCode1
Learning to Tokenize for Generative RetrievalCode1
A Reproducible Extraction of Training Images from Diffusion ModelsCode1
CaseLink: Inductive Graph Learning for Legal Case RetrievalCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1BM25SQueries per second183.53Unverified
2ElasticsearchQueries per second21.8Unverified
3BM25-PTQueries per second6.49Unverified
4Rank-BM25Queries per second1.18Unverified
#ModelMetricClaimedVerifiedStatus
1BM25SQueries per second20.88Unverified
2ElasticsearchQueries per second7.11Unverified
3Rank-BM25Queries per second0.04Unverified
#ModelMetricClaimedVerifiedStatus
1BM25SQueries per second41.85Unverified
2ElasticsearchQueries per second12.16Unverified
3Rank-BM25Queries per second0.1Unverified
#ModelMetricClaimedVerifiedStatus
1FLMRRecall@589.32Unverified
2RA-VQARecall@582.84Unverified
#ModelMetricClaimedVerifiedStatus
1PreFLMRRecall@562.1Unverified
#ModelMetricClaimedVerifiedStatus
1CLIP-KIStext-to-video Mean Rank30Unverified
#ModelMetricClaimedVerifiedStatus
1CLIP4OutfitRecall@57.59Unverified
#ModelMetricClaimedVerifiedStatus
1MetaGen Blended RAGAccuracy (Top-1)82.1Unverified
#ModelMetricClaimedVerifiedStatus
1MetaGen Blended RAGAccuracy (Top-1)82.1Unverified
#ModelMetricClaimedVerifiedStatus
1COLTCOMP@84.55Unverified
#ModelMetricClaimedVerifiedStatus
1hello0L1,121,222Unverified