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RAG

Retrieval-Augmented Generation (RAG) is a task that combines the strengths of both retrieval-based models and generation-based models. In this approach, a retrieval system selects relevant documents or passages from a large corpus, and a generation model, typically a neural language model, uses the retrieved information to generate a response. This method enhances the accuracy and coherence of generated text, especially in tasks requiring detailed knowledge or long context handling.

RAG is particularly useful in open-domain question answering, knowledge-grounded dialogue, and summarization tasks. The retrieval step helps the model to access and incorporate external information, making it less reliant on memorized knowledge and better suited for generating responses based on the latest or domain-specific information.

The performance of RAG systems is usually measured using metrics such as precision, recall, F1 score, BLEU score, and exact match. Some popular datasets for evaluating RAG models include Natural Questions, MS MARCO, TriviaQA, and SQuAD.

Papers

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Showing 551–575 of 2111 papers

TitleStatusHype
MoK-RAG: Mixture of Knowledge Paths Enhanced Retrieval-Augmented Generation for Embodied AI Environments—0
RAD: Retrieval-Augmented Decision-Making of Meta-Actions with Vision-Language Models in Autonomous Driving—0
Beyond Single Pass, Looping Through Time: KG-IRAG with Iterative Knowledge Retrieval—0
RAG-RL: Advancing Retrieval-Augmented Generation via RL and Curriculum Learning—0
OSCAR: Online Soft Compression And Reranking—0
LLM-Match: An Open-Sourced Patient Matching Model Based on Large Language Models and Retrieval-Augmented Generation—0
MES-RAG: Bringing Multi-modal, Entity-Storage, and Secure Enhancements to RAGCode0
Privacy-Aware RAG: Secure and Isolated Knowledge Retrieval—0
Generative AI for Software Architecture. Applications, Trends, Challenges, and Future Directions—0
Logic-RAG: Augmenting Large Multimodal Models with Visual-Spatial Knowledge for Road Scene UnderstandingCode1
Datrics Text2SQL. A Framework for Natural Language to SQL Query GenerationCode2
Integrating Chain-of-Thought and Retrieval Augmented Generation Enhances Rare Disease Diagnosis from Clinical Notes—0
Agentic Search Engine for Real-Time IoT DataCode0
TFHE-Coder: Evaluating LLM-agentic Fully Homomorphic Encryption Code Generation—0
Relevance Isn't All You Need: Scaling RAG Systems With Inference-Time Compute Via Multi-Criteria RerankingCode14
RAG-KG-IL: A Multi-Agent Hybrid Framework for Reducing Hallucinations and Enhancing LLM Reasoning through RAG and Incremental Knowledge Graph Learning Integration—0
Agent-Enhanced Large Language Models for Researching Political InstitutionsCode0
MUSS: Multilevel Subset Selection for Relevance and Diversity—0
AIstorian lets AI be a historian: A KG-powered multi-agent system for accurate biography generationCode0
FG-RAG: Enhancing Query-Focused Summarization with Context-Aware Fine-Grained Graph RAGCode0
AttentionRAG: Attention-Guided Context Pruning in Retrieval-Augmented Generation—0
Taxonomic Reasoning for Rare Arthropods: Combining Dense Image Captioning and RAG for Interpretable Classification—0
SurgRAW: Multi-Agent Workflow with Chain-of-Thought Reasoning for Surgical Intelligence—0
Retrieval-Augmented Generation with Hierarchical KnowledgeCode4
ClaimTrust: Propagation Trust Scoring for RAG Systems—0
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