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 1401–1425 of 14297 papers

TitleStatusHype
Context-aware Decoding Reduces Hallucination in Query-focused SummarizationCode1
Context Awareness Gate For Retrieval Augmented GenerationCode1
Context-I2W: Mapping Images to Context-dependent Words for Accurate Zero-Shot Composed Image RetrievalCode1
ConText-CIR: Learning from Concepts in Text for Composed Image RetrievalCode1
CARE: a Benchmark Suite for the Classification and Retrieval of EnzymesCode1
Temporal Context Aggregation for Video Retrieval with Contrastive LearningCode1
Data Curation Alone Can Stabilize In-context LearningCode1
1st Place Solution to Google Landmark Retrieval 2020Code1
Hot-Refresh Model Upgrades with Regression-Alleviating Compatible Training in Image RetrievalCode1
Enhancing Recipe Retrieval with Foundation Models: A Data Augmentation PerspectiveCode1
CascadER: Cross-Modal Cascading for Knowledge Graph Link PredictionCode1
ConTextual Masked Auto-Encoder for Dense Passage RetrievalCode1
Contextualized Sparse Representations for Real-Time Open-Domain Question AnsweringCode1
Contextual Lensing of Universal Sentence RepresentationsCode1
Drone Referring Localization: An Efficient Heterogeneous Spatial Feature Interaction Method For UAV Self-LocalizationCode1
HowTo100M: Learning a Text-Video Embedding by Watching Hundred Million Narrated Video ClipsCode1
Few-Shot Generative Conversational Query RewritingCode1
Contextual Similarity Aggregation with Self-attention for Visual Re-rankingCode1
Few-Shot Bot: Prompt-Based Learning for Dialogue SystemsCode1
A Symmetric Dual Encoding Dense Retrieval Framework for Knowledge-Intensive Visual Question AnsweringCode1
FETA: Towards Specializing Foundation Models for Expert Task ApplicationsCode1
Few-Shot Conversational Dense RetrievalCode1
Few-Shot Recognition via Stage-Wise Retrieval-Augmented FinetuningCode1
Can large language models reason about medical questions?Code1
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