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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 1476–1500 of 2111 papers

TitleStatusHype
Application of NotebookLM, a Large Language Model with Retrieval-Augmented Generation, for Lung Cancer Staging—0
Novel Preprocessing Technique for Data Embedding in Engineering Code Generation Using Large Language Model—0
A Proposal for Evaluating the Operational Risk for ChatBots based on Large Language Models—0
A Proposed Large Language Model-Based Smart Search for Archive System—0
ARAG: Agentic Retrieval Augmented Generation for Personalized Recommendation—0
A RAG Approach for Generating Competency Questions in Ontology Engineering—0
A RAG-Based Institutional Assistant—0
A RAG-based Question Answering System Proposal for Understanding Islam: MufassirQAS LLM—0
ARCeR: an Agentic RAG for the Automated Definition of Cyber Ranges—0
ArchRAG: Attributed Community-based Hierarchical Retrieval-Augmented Generation—0
ARCS: Agentic Retrieval-Augmented Code Synthesis with Iterative Refinement—0
A Reasoning-Focused Legal Retrieval Benchmark—0
Are Large Language Models In-Context Graph Learners?—0
A Reliable Knowledge Processing Framework for Combustion Science using Foundation Models—0
Are LLMs Prescient? A Continuous Evaluation using Daily News as the Oracle—0
A Retrieval-Augmented Generation Framework for Academic Literature Navigation in Data Science—0
A review of faithfulness metrics for hallucination assessment in Large Language Models—0
A Review on Scientific Knowledge Extraction using Large Language Models in Biomedical Sciences—0
ARise: Towards Knowledge-Augmented Reasoning via Risk-Adaptive Search—0
Artificial Intelligence as the New Hacker: Developing Agents for Offensive Security—0
ArtRAG: Retrieval-Augmented Generation with Structured Context for Visual Art Understanding—0
A Simple Architecture for Enterprise Large Language Model Applications based on Role based security and Clearance Levels using Retrieval-Augmented Generation or Mixture of Experts—0
Ask-EDA: A Design Assistant Empowered by LLM, Hybrid RAG and Abbreviation De-hallucination—0
A Socratic RAG Approach to Connect Natural Language Queries on Research Topics with Knowledge Organization Systems—0
ASRank: Zero-Shot Re-Ranking with Answer Scent for Document Retrieval—0
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