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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 3876–3900 of 14297 papers

TitleStatusHype
MORE: Multi-mOdal REtrieval Augmented Generative Commonsense Reasoning—0
ARL2: Aligning Retrievers for Black-box Large Language Models via Self-guided Adaptive Relevance LabelingCode0
The Effectiveness of Graph Contrastive Learning on Mathematical Information RetrievalCode0
Fine-Grained Modeling of Narrative Context: A Coherence Perspective via Retrospective Questions—0
Self-DC: When to Reason and When to Act? Self Divide-and-Conquer for Compositional Unknown Questions—0
Distinctive Image Captioning: Leveraging Ground Truth Captions in CLIP Guided Reinforcement LearningCode1
A Two-Stage Dual-Path Framework for Text Tampering Detection and Recognition—0
Science Checker Reloaded: A Bidirectional Paradigm for Transparency and Logical ReasoningCode0
Event-aware Video Corpus Moment Retrieval—0
Improving Video Corpus Moment Retrieval with Partial Relevance EnhancementCode0
PIRB: A Comprehensive Benchmark of Polish Dense and Hybrid Text Retrieval Methods—0
DINOBot: Robot Manipulation via Retrieval and Alignment with Vision Foundation Models—0
Effective and Efficient Conversation Retrieval for Dialogue State Tracking with Implicit Text Summaries—0
Stochastic Graph Heat Modelling for Diffusion-based Connectivity Retrieval—0
Interpreting Conversational Dense Retrieval by Rewriting-Enhanced Inversion of Session EmbeddingCode1
SoftQE: Learned Representations of Queries Expanded by LLMs—0
Towards Trustworthy Reranking: A Simple yet Effective Abstention MechanismCode0
FormulaReasoning: A Dataset for Formula-Based Numerical ReasoningCode0
Unlocking Insights: Semantic Search in Jupyter Notebooks—0
Understanding and Mitigating the Threat of Vec2Text to Dense Retrieval SystemsCode1
Benchmarking Retrieval-Augmented Generation for MedicineCode4
Heterogeneous Graph Reasoning for Fact Checking over Texts and TablesCode1
Standardize: Aligning Language Models with Expert-Defined Standards for Content GenerationCode0
FeB4RAG: Evaluating Federated Search in the Context of Retrieval Augmented Generation—0
BIDER: Bridging Knowledge Inconsistency for Efficient Retrieval-Augmented LLMs via Key Supporting Evidence—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