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 1451–1475 of 14297 papers

TitleStatusHype
FlexiViT: One Model for All Patch SizesCode1
Correlation Verification for Image RetrievalCode1
Forward Compatible Training for Large-Scale Embedding Retrieval SystemsCode1
COSA: Concatenated Sample Pretrained Vision-Language Foundation ModelCode1
C-RAG: Certified Generation Risks for Retrieval-Augmented Language ModelsCode1
Enhancing Recipe Retrieval with Foundation Models: A Data Augmentation PerspectiveCode1
CoVR-2: Automatic Data Construction for Composed Video RetrievalCode1
Are We There Yet? Evaluating State-of-the-Art Neural Network based Geoparsers Using EUPEG as a Benchmarking PlatformCode1
CARE: a Benchmark Suite for the Classification and Retrieval of EnzymesCode1
Filling the Gap of Utterance-aware and Speaker-aware Representation for Multi-turn DialogueCode1
M3-Jepa: Multimodal Alignment via Multi-directional MoE based on the JEPA frameworkCode1
AtomR: Atomic Operator-Empowered Large Language Models for Heterogeneous Knowledge ReasoningCode1
Adaptive Confidence Multi-View Hashing for Multimedia RetrievalCode1
Creating Something from Nothing: Unsupervised Knowledge Distillation for Cross-Modal HashingCode1
CREPE: A Convolutional Representation for Pitch EstimationCode1
CREPE: Can Vision-Language Foundation Models Reason Compositionally?Code1
Data Curation Alone Can Stabilize In-context LearningCode1
Crisscrossed Captions: Extended Intramodal and Intermodal Semantic Similarity Judgments for MS-COCOCode1
ATR4S: Toolkit with State-of-the-art Automatic Terms Recognition Methods in ScalaCode1
AT-RAG: An Adaptive RAG Model Enhancing Query Efficiency with Topic Filtering and Iterative ReasoningCode1
Filtering before Iteratively Referring for Knowledge-Grounded Response Selection in Retrieval-Based ChatbotsCode1
Few-Shot Recognition via Stage-Wise Retrieval-Augmented FinetuningCode1
3rd Place Solution to "Google Landmark Retrieval 2020"Code1
Cross-domain Retrieval in the Legal and Patent Domains: a Reproducibility StudyCode1
Can Retriever-Augmented Language Models Reason? The Blame Game Between the Retriever and the Language ModelCode1
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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