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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 1401–1425 of 2111 papers

TitleStatusHype
Addressing Hallucinations with RAG and NMISS in Italian Healthcare LLM Chatbots—0
Adopting Large Language Models to Automated System Integration—0
Adopting RAG for LLM-Aided Future Vehicle Design—0
A Driver Advisory System Based on Large Language Model for High-speed Train—0
D2S-FLOW: Automated Parameter Extraction from Datasheets for SPICE Model Generation Using Large Language Models—0
Advanced ingestion process powered by LLM parsing for RAG system—0
Advanced RAG Models with Graph Structures: Optimizing Complex Knowledge Reasoning and Text Generation—0
Advanced Real-Time Fraud Detection Using RAG-Based LLMs—0
Advanced System Integration: Analyzing OpenAPI Chunking for Retrieval-Augmented Generation—0
Advancing Conversational Psychotherapy: Integrating Privacy, Dual-Memory, and Domain Expertise with Large Language Models—0
GenDFIR: Advancing Cyber Incident Timeline Analysis Through Retrieval Augmented Generation and Large Language Models—0
Advancing Retrieval-Augmented Generation for Persian: Development of Language Models, Comprehensive Benchmarks, and Best Practices for Optimization—0
Advancing Vietnamese Information Retrieval with Learning Objective and Benchmark—0
Adversarial Databases Improve Success in Retrieval-based Large Language Models—0
Adversarial Threat Vectors and Risk Mitigation for Retrieval-Augmented Generation Systems—0
AesopAgent: Agent-driven Evolutionary System on Story-to-Video Production—0
A Fine-tuning Enhanced RAG System with Quantized Influence Measure as AI Judge—0
After Retrieval, Before Generation: Enhancing the Trustworthiness of Large Language Models in RAG—0
A GEN AI Framework for Medical Note Generation—0
A General Retrieval-Augmented Generation Framework for Multimodal Case-Based Reasoning Applications—0
Agentic AI-Driven Technical Troubleshooting for Enterprise Systems: A Novel Weighted Retrieval-Augmented Generation Paradigm—0
Agentic Multimodal AI for Hyperpersonalized B2B and B2C Advertising in Competitive Markets: An AI-Driven Competitive Advertising Framework—0
Agentic Retrieval-Augmented Generation for Time Series Analysis—0
Agentic Verification for Ambiguous Query Disambiguation—0
AgentNet: Decentralized Evolutionary Coordination for LLM-based Multi-Agent Systems—0
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