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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 601–625 of 2111 papers

TitleStatusHype
ASRank: Zero-Shot Re-Ranking with Answer Scent for Document Retrieval—0
CollEX -- A Multimodal Agentic RAG System Enabling Interactive Exploration of Scientific Collections—0
Collapse of Dense Retrievers: Short, Early, and Literal Biases Outranking Factual Evidence—0
A Socratic RAG Approach to Connect Natural Language Queries on Research Topics with Knowledge Organization Systems—0
Accurate and Energy Efficient: Local Retrieval-Augmented Generation Models Outperform Commercial Large Language Models in Medical Tasks—0
Establishing Performance Baselines in Fine-Tuning, Retrieval-Augmented Generation and Soft-Prompting for Non-Specialist LLM Users—0
Cognitive-Aligned Document Selection for Retrieval-augmented Generation—0
Agentic Retrieval-Augmented Generation for Time Series Analysis—0
CodeXEmbed: A Generalist Embedding Model Family for Multiligual and Multi-task Code Retrieval—0
Ask-EDA: A Design Assistant Empowered by LLM, Hybrid RAG and Abbreviation De-hallucination—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
Multi-Level Querying using A Knowledge Pyramid—0
Evaluating and Enhancing Large Language Models Performance in Domain-specific Medicine: Osteoarthritis Management with DocOA—0
Evaluating Memory in LLM Agents via Incremental Multi-Turn Interactions—0
Code Graph Model (CGM): A Graph-Integrated Large Language Model for Repository-Level Software Engineering Tasks—0
Clustering Algorithms and RAG Enhancing Semi-Supervised Text Classification with Large LLMs—0
ArtRAG: Retrieval-Augmented Generation with Structured Context for Visual Art Understanding—0
Artificial Intelligence as the New Hacker: Developing Agents for Offensive Security—0
CL-RAG: Bridging the Gap in Retrieval-Augmented Generation with Curriculum Learning—0
Accelerating Retrieval-Augmented Generation—0
CLI-RAG: A Retrieval-Augmented Framework for Clinically Structured and Context Aware Text Generation with LLMs—0
Agentic Multimodal AI for Hyperpersonalized B2B and B2C Advertising in Competitive Markets: An AI-Driven Competitive Advertising Framework—0
ENWAR: A RAG-empowered Multi-Modal LLM Framework for Wireless Environment Perception—0
Faculty Perspectives on the Potential of RAG in Computer Science Higher Education—0
ERATTA: Extreme RAG for Table To Answers with Large Language Models—0
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