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 54015425 of 14297 papers

TitleStatusHype
Book QA: Stories of Challenges and Opportunities0
Divide and Conquer: Text Semantic Matching with Disentangled Keywords and Intents0
DivGraphPointer: A Graph Pointer Network for Extracting Diverse Keyphrases0
An NMF Perspective on Binary Hashing0
Advancing Vietnamese Information Retrieval with Learning Objective and Benchmark0
Diversity driven Query Rewriting in Search Advertising0
An Issue-oriented Syllabus Retrieval System using Terminology-based Syllabus Structuring and Visualization0
Diversify-verify-adapt: Efficient and Robust Retrieval-Augmented Ambiguous Question Answering0
Diversifying Reply Suggestions using a Matching-Conditional Variational Autoencoder0
Diverse Yet Efficient Retrieval using Hash Functions0
BM25 Query Augmentation Learned End-to-End0
ANISE: Assembly-based Neural Implicit Surface rEconstruction0
A Comparative Analysis of Faithfulness Metrics and Humans in Citation Evaluation0
Diverse Multi-Answer Retrieval with Determinantal Point Processes0
Blowfish: Topological and statistical signatures for quantifying ambiguity in semantic search0
Diverse legal case search0
Block-Sparse Recovery Network for Two-Dimensional Harmonic Retrieval0
An Invitation to Hypercomplex Phase Retrieval: Theory and Applications0
Distributional Vision-Language Alignment by Cauchy-Schwarz Divergence0
Distributional Thesauri for Information Retrieval and vice versa0
Blind Interference Alignment for Private Information Retrieval0
An Investigative Study of Multi-Modal Cross-Lingual Retrieval0
Distributionally Robust Multi-Output Regression Ranking0
Distribution-Aligned Fine-Tuning for Efficient Neural Retrieval0
BlendFilter: Advancing Retrieval-Augmented Large Language Models via Query Generation Blending and Knowledge Filtering0
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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