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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 1451–1500 of 14297 papers

TitleStatusHype
Retrieval-Augmented Generation for Mobile Edge Computing via Large Language Model—0
GASLITEing the Retrieval: Exploring Vulnerabilities in Dense Embedding-based SearchCode1
EdgeRAG: Online-Indexed RAG for Edge Devices—0
Unsupervised dense retrieval with conterfactual contrastive learning—0
Language-based Audio Retrieval with Co-Attention Networks—0
Towards Identity-Aware Cross-Modal Retrieval: a Dataset and a BaselineCode0
Enhanced Multimodal RAG-LLM for Accurate Visual Question Answering—0
Length-Aware DETR for Robust Moment RetrievalCode1
GliLem: Leveraging GliNER for Contextualized Lemmatization in Estonian—0
STAYKATE: Hybrid In-Context Example Selection Combining Representativeness Sampling and Retrieval-based Approach -- A Case Study on Science Domains—0
FashionFAE: Fine-grained Attributes Enhanced Fashion Vision-Language Pre-training—0
Long Context vs. RAG for LLMs: An Evaluation and RevisitsCode1
Optimizing Multi-Stage Language Models for Effective Text Retrieval—0
Jasper and Stella: distillation of SOTA embedding modelsCode1
Semantic Residual for Multimodal Unified Discrete Representation—0
Reversed in Time: A Novel Temporal-Emphasized Benchmark for Cross-Modal Video-Text RetrievalCode0
RAG with Differential PrivacyCode1
ViPCap: Retrieval Text-Based Visual Prompts for Lightweight Image CaptioningCode0
On the Robustness of Generative Information Retrieval ModelsCode0
Research Experiment on Multi-Model Comparison for Chinese Text Classification Tasks—0
FOR: Finetuning for Object Level Open Vocabulary Image Retrieval—0
DynaGRAG | Exploring the Topology of Information for Advancing Language Understanding and Generation in Graph Retrieval-Augmented Generation—0
Efficient Long Context Language Model Retrieval with Compression—0
RaSeRec: Retrieval-Augmented Sequential RecommendationCode1
Improving Factuality with Explicit Working Memory—0
Neural auto-association with optimal Bayesian learning—0
CypherBench: Towards Precise Retrieval over Full-scale Modern Knowledge Graphs in the LLM EraCode1
Molly: Making Large Language Model Agents Solve Python Problem More Logically—0
Pirates of the RAG: Adaptively Attacking LLMs to Leak Knowledge Bases—0
GeAR: Graph-enhanced Agent for Retrieval-augmented Generation—0
ERVD: An Efficient and Robust ViT-Based Distillation Framework for Remote Sensing Image RetrievalCode0
Contrato360 2.0: A Document and Database-Driven Question-Answer System using Large Language Models and Agents—0
Dynamic Multi-Agent Orchestration and Retrieval for Multi-Source Question-Answer Systems using Large Language Models—0
Leveraging Memory Retrieval to Enhance LLM-based Generative Recommendation—0
SyNeg: LLM-Driven Synthetic Hard-Negatives for Dense Retrieval—0
A Survey of Query Optimization in Large Language Models—0
RAGONITE: Iterative Retrieval on Induced Databases and Verbalized RDF for Conversational QA over KGs with RAG—0
COBRA: COmBinatorial Retrieval Augmentation for Few-Shot Adaptation—0
Efficient fine-tuning methodology of text embedding models for information retrieval: contrastive learning penalty (clp)Code1
Iterative NLP Query Refinement for Enhancing Domain-Specific Information Retrieval: A Case Study in Career ServicesCode0
MINTQA: A Multi-Hop Question Answering Benchmark for Evaluating LLMs on New and Tail KnowledgeCode0
GME: Improving Universal Multimodal Retrieval by Multimodal LLMs—0
Where am I? Cross-View Geo-localization with Natural Language DescriptionsCode2
A Reality Check on Context Utilisation for Retrieval-Augmented GenerationCode0
AlzheimerRAG: Multimodal Retrieval Augmented Generation for PubMed articles—0
Large Language Model Can Be a Foundation for Hidden Rationale-Based RetrievalCode0
Towards More Robust Retrieval-Augmented Generation: Evaluating RAG Under Adversarial Poisoning AttacksCode0
TimeRAG: BOOSTING LLM Time Series Forecasting via Retrieval-Augmented Generation—0
Formal Language Knowledge Corpus for Retrieval Augmented Generation—0
Speech Retrieval-Augmented Generation without Automatic Speech Recognition—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