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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 3151–3175 of 14297 papers

TitleStatusHype
Seeking to Collide: Online Safety-Critical Scenario Generation for Autonomous Driving with Retrieval Augmented Large Language Models—0
MateICL: Mitigating Attention Dispersion in Large-Scale In-Context LearningCode0
Retrieval-Augmented Generation in Biomedicine: A Survey of Technologies, Datasets, and Clinical Applications—0
CHORUS: Zero-shot Hierarchical Retrieval and Orchestration for Generating Linear Programming Code—0
Investigating Task Arithmetic for Zero-Shot Information RetrievalCode0
A Multi-Granularity Retrieval Framework for Visually-Rich Documents—0
Scalable Unit Harmonization in Medical Informatics Using Bi-directional Transformers and Bayesian-Optimized BM25 and Sentence Embedding Retrieval—0
Empowering Agentic Video Analytics Systems with Video Language Models—0
EnronQA: Towards Personalized RAG over Private Documents—0
TartuNLP at SemEval-2025 Task 5: Subject Tagging as Two-Stage Information Retrieval—0
Traceback of Poisoning Attacks to Retrieval-Augmented Generation—0
Homa at SemEval-2025 Task 5: Aligning Librarian Records with OntoAligner for Subject Tagging—0
Improving Retrieval-Augmented Neural Machine Translation with Monolingual Data—0
Efficient Conversational Search via Topical Locality in Dense RetrievalCode0
Retrieval-Enhanced Few-Shot Prompting for Speech Event Extraction—0
Talk Before You Retrieve: Agent-Led Discussions for Better RAG in Medical QACode0
Learning Universal User Representations Leveraging Cross-domain User Intent at Snapchat—0
Effective Inference-Free Retrieval for Learned Sparse Representations—0
Towards Robust and Generalizable Gerchberg Saxton based Physics Inspired Neural Networks for Computer Generated Holography: A Sensitivity Analysis Framework—0
Information Retrieval in the Age of Generative AI: The RGB ModelCode0
Federated One-Shot Learning with Data Privacy and Objective-Hiding—0
ARCS: Agentic Retrieval-Augmented Code Synthesis with Iterative Refinement—0
Reconstructing Context: Evaluating Advanced Chunking Strategies for Retrieval-Augmented GenerationCode0
Can LLMs Be Trusted for Evaluating RAG Systems? A Survey of Methods and Datasets—0
OpenTCM: A GraphRAG-Empowered LLM-based System for Traditional Chinese Medicine Knowledge Retrieval and Diagnosis—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