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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 426–450 of 2111 papers

TitleStatusHype
Adopting Large Language Models to Automated System Integration—0
AI-University: An LLM-based platform for instructional alignment to scientific classroomsCode0
RAG-VR: Leveraging Retrieval-Augmented Generation for 3D Question Answering in VR EnvironmentsCode0
A System for Comprehensive Assessment of RAG FrameworksCode0
ConceptFormer: Towards Efficient Use of Knowledge-Graph Embeddings in Large Language Models—0
CollEX -- A Multimodal Agentic RAG System Enabling Interactive Exploration of Scientific Collections—0
MRD-RAG: Enhancing Medical Diagnosis with Multi-Round Retrieval-Augmented GenerationCode1
AgentAda: Skill-Adaptive Data Analytics for Tailored Insight DiscoveryCode1
PR-Attack: Coordinated Prompt-RAG Attacks on Retrieval-Augmented Generation in Large Language Models via Bilevel Optimization—0
Poly-Vector Retrieval: Reference and Content Embeddings for Legal Documents—0
Evaluating Retrieval Augmented Generative Models for Document Queries in Transportation Safety—0
Graph-based Approaches and Functionalities in Retrieval-Augmented Generation: A Comprehensive Survey—0
PathGPT: Leveraging Large Language Models for Personalized Route Generation—0
Retrieval Augmented Generation with Collaborative Filtering for Personalized Text GenerationCode1
Decentralizing AI Memory: SHIMI, a Semantic Hierarchical Memory Index for Scalable Agent Reasoning—0
Simplifying Data Integration: SLM-Driven Systems for Unified Semantic Queries Across Heterogeneous Databases—0
MicroNN: An On-device Disk-resident Updatable Vector Database—0
AI for Climate Finance: Agentic Retrieval and Multi-Step Reasoning for Early Warning System Investments—0
Bridging Industrial Expertise and XR with LLM-Powered Conversational Agents—0
Evaluating Knowledge Graph Based Retrieval Augmented Generation Methods under Knowledge Incompleteness—0
Leveraging LLMs for Utility-Focused Annotation: Reducing Manual Effort for Retrieval and RAG—0
Collab-RAG: Boosting Retrieval-Augmented Generation for Complex Question Answering via White-Box and Black-Box LLM CollaborationCode1
GraphRAFT: Retrieval Augmented Fine-Tuning for Knowledge Graphs on Graph DatabasesCode0
CCSK:Cognitive Convection of Self-Knowledge Based Retrieval Augmentation for Large Language Models—0
RS-RAG: Bridging Remote Sensing Imagery and Comprehensive Knowledge with a Multi-Modal Dataset and Retrieval-Augmented Generation Model—0
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