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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 826–850 of 2111 papers

TitleStatusHype
PR-Attack: Coordinated Prompt-RAG Attacks on Retrieval-Augmented Generation in Large Language Models via Bilevel Optimization—0
Evaluating Retrieval Augmented Generative Models for Document Queries in Transportation Safety—0
Poly-Vector Retrieval: Reference and Content Embeddings for Legal Documents—0
MicroNN: An On-device Disk-resident Updatable Vector Database—0
Graph-based Approaches and Functionalities in Retrieval-Augmented Generation: A Comprehensive Survey—0
PathGPT: Leveraging Large Language Models for Personalized Route Generation—0
Simplifying Data Integration: SLM-Driven Systems for Unified Semantic Queries Across Heterogeneous Databases—0
Decentralizing AI Memory: SHIMI, a Semantic Hierarchical Memory Index for Scalable Agent Reasoning—0
AI for Climate Finance: Agentic Retrieval and Multi-Step Reasoning for Early Warning System Investments—0
RS-RAG: Bridging Remote Sensing Imagery and Comprehensive Knowledge with a Multi-Modal Dataset and Retrieval-Augmented Generation Model—0
CCSK:Cognitive Convection of Self-Knowledge Based Retrieval Augmentation for Large Language Models—0
GraphRAFT: Retrieval Augmented Fine-Tuning for Knowledge Graphs on Graph DatabasesCode0
Evaluating Knowledge Graph Based Retrieval Augmented Generation Methods under Knowledge Incompleteness—0
Bridging Industrial Expertise and XR with LLM-Powered Conversational Agents—0
Leveraging LLMs for Utility-Focused Annotation: Reducing Manual Effort for Retrieval and RAG—0
Don't Lag, RAG: Training-Free Adversarial Detection Using RAG—0
Hierarchical Planning for Complex Tasks with Knowledge Graph-RAG and Symbolic Verification—0
Driving-RAG: Driving Scenarios Embedding, Search, and RAG Applications—0
QE-RAG: A Robust Retrieval-Augmented Generation Benchmark for Query Entry Errors—0
Generative AI Enhanced Financial Risk Management Information RetrievalCode0
Practical Poisoning Attacks against Retrieval-Augmented Generation—0
Multilingual Retrieval-Augmented Generation for Knowledge-Intensive Task—0
Talk2X -- An Open-Source Toolkit Facilitating Deployment of LLM-Powered Chatbots on the WebCode0
Rotation Invariance in Floor Plan Digitization using Zernike Moments—0
NAACL2025 Tutorial: Adaptation of Large Language Models—0
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