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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 1301–1325 of 2111 papers

TitleStatusHype
Bridging Relevance and Reasoning: Rationale Distillation in Retrieval-Augmented Generation—0
Leveraging Graph-RAG and Prompt Engineering to Enhance LLM-Based Automated Requirement Traceability and Compliance Checks—0
Ontology-Aware RAG for Improved Question-Answering in Cybersecurity Education—0
Privacy-Preserving Customer Support: A Framework for Secure and Scalable Interactions—0
RAG-based Question Answering over Heterogeneous Data and Text—0
SiReRAG: Indexing Similar and Related Information for Multihop Reasoning—0
Efficient VoIP Communications through LLM-based Real-Time Speech Reconstruction and Call Prioritization for Emergency Services—0
LLM as HPC Expert: Extending RAG Architecture for HPC Data—0
Accelerating Manufacturing Scale-Up from Material Discovery Using Agentic Web Navigation and Retrieval-Augmented AI for Process Engineering Schematics Design—0
A Collaborative Multi-Agent Approach to Retrieval-Augmented Generation Across Diverse Data—0
Mixture-of-PageRanks: Replacing Long-Context with Real-Time, Sparse GraphRAG—0
DECO: Life-Cycle Management of Enterprise-Grade Copilots—0
GEE-OPs: An Operator Knowledge Base for Geospatial Code Generation on the Google Earth Engine Platform Powered by Large Language Models—0
SLA Management in Reconfigurable Multi-Agent RAG: A Systems Approach to Question Answering—0
100% Elimination of Hallucinations on RAGTruth for GPT-4 and GPT-3.5 Turbo—0
TOBUGraph: Knowledge Graph-Based Retrieval for Enhanced LLM Performance Beyond RAG—0
Enhancing Cross-Language Code Translation via Task-Specific Embedding Alignment in Retrieval-Augmented Generation—0
Privacy-Preserving Retrieval-Augmented Generation with Differential Privacy—0
QueEn: A Large Language Model for Quechua-English Translation—0
Exploring AI Text Generation, Retrieval-Augmented Generation, and Detection Technologies: a Comprehensive Overview—0
Targeting the Core: A Simple and Effective Method to Attack RAG-based Agents via Direct LLM Manipulation—0
Addressing Hallucinations with RAG and NMISS in Italian Healthcare LLM Chatbots—0
Leveraging Large Language Models to Generate Course-specific Semantically Annotated Learning Objects—0
Advancing Conversational Psychotherapy: Integrating Privacy, Dual-Memory, and Domain Expertise with Large Language Models—0
A Review on Scientific Knowledge Extraction using Large Language Models in Biomedical Sciences—0
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