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 4076–4100 of 14297 papers

TitleStatusHype
DoQA -- Accessing Domain-Specific FAQs via Conversational QA—0
Enhancing Question Answering Precision with Optimized Vector Retrieval and Instructions—0
DoQA - Accessing Domain-Specific FAQs via Conversational QA—0
CvxNet: Learnable Convex Decomposition—0
CVL-DataBase: An Off-Line Database for Writer Retrieval, Writer Identification and Word Spotting—0
Automatic Feature Weight Determination using Indexing and Pseudo-Relevance Feedback for Multi-feature Content-Based Image Retrieval—0
Do Retrieval-Augmented Language Models Adapt to Varying User Needs?—0
CV4Code: Sourcecode Understanding via Visual Code Representations—0
Bridging Law and Data: Augmenting Reasoning via a Semi-Structured Dataset with IRAC methodology—0
Customizing Knowledge Graph Embedding to Improve Clinical Study Recommendation—0
Automatic Fact-Checking with Frame-Semantics—0
Doubly-stochastic mining for heterogeneous retrieval—0
Do ``Undocumented Workers'' == ``Illegal Aliens''? Differentiating Denotation and Connotation in Vector Spaces—0
Analysing Word Importance for Image Annotation—0
Bridging Queries and Tables through Entities in Table Retrieval—0
Automatic Face Understanding: Recognizing Families in Photos—0
Do We Really Need Scene-specific Pose Encoders?—0
Bridging Search and Recommendation in Generative Retrieval: Does One Task Help the Other?—0
Customized Nonlinear Bandits for Online Response Selection in Neural Conversation Models—0
Automatic Face Reenactment—0
Custom Data Augmentation for low resource ASR using Bark and Retrieval-Based Voice Conversion—0
CuSINeS: Curriculum-driven Structure Induced Negative Sampling for Statutory Article Retrieval—0
A data-driven model of explanations for a chatbot that helps to practice conversation in a foreign language—0
Enhancing Q&A Text Retrieval with Ranking Models: Benchmarking, fine-tuning and deploying Rerankers for RAG—0
Enhancing Relevance of Embedding-based Retrieval at Walmart—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