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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 1026–1050 of 2111 papers

TitleStatusHype
OG-RAG: Ontology-Grounded Retrieval-Augmented Generation For Large Language Models—0
Assessing the Robustness of Retrieval-Augmented Generation Systems in K-12 Educational Question Answering with Knowledge Discrepancies—0
Context Canvas: Enhancing Text-to-Image Diffusion Models with Knowledge Graph-Based RAG—0
Leveraging Graph-RAG and Prompt Engineering to Enhance LLM-Based Automated Requirement Traceability and Compliance Checks—0
Federated In-Context LLM Agent Learning—0
Bridging Relevance and Reasoning: Rationale Distillation in Retrieval-Augmented Generation—0
Ontology-Aware RAG for Improved Question-Answering in Cybersecurity Education—0
RAG-based Question Answering over Heterogeneous Data and Text—0
Granite GuardianCode2
OmniDocBench: Benchmarking Diverse PDF Document Parsing with Comprehensive AnnotationsCode5
Privacy-Preserving Customer Support: A Framework for Secure and Scalable Interactions—0
Adapting to Non-Stationary Environments: Multi-Armed Bandit Enhanced Retrieval-Augmented Generation on Knowledge GraphsCode1
LLM as HPC Expert: Extending RAG Architecture for HPC Data—0
Efficient VoIP Communications through LLM-based Real-Time Speech Reconstruction and Call Prioritization for Emergency Services—0
SiReRAG: Indexing Similar and Related Information for Multihop Reasoning—0
Retrieving Semantics from the Deep: an RAG Solution for Gesture SynthesisCode2
Mixture-of-PageRanks: Replacing Long-Context with Real-Time, Sparse GraphRAG—0
A Collaborative Multi-Agent Approach to Retrieval-Augmented Generation Across Diverse Data—0
Accelerating Manufacturing Scale-Up from Material Discovery Using Agentic Web Navigation and Retrieval-Augmented AI for Process Engineering Schematics Design—0
DECO: Life-Cycle Management of Enterprise-Grade Copilots—0
SLA Management in Reconfigurable Multi-Agent RAG: A Systems Approach to Question Answering—0
GEE-OPs: An Operator Knowledge Base for Geospatial Code Generation on the Google Earth Engine Platform Powered by Large Language Models—0
KG-Retriever: Efficient Knowledge Indexing for Retrieval-Augmented Large Language ModelsCode1
TOBUGraph: Knowledge Graph-Based Retrieval for Enhanced LLM Performance Beyond RAG—0
100% Elimination of Hallucinations on RAGTruth for GPT-4 and GPT-3.5 Turbo—0
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