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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 1–25 of 2111 papers

TitleStatusHype
A Survey of Context Engineering for Large Language Models—0
Developing Visual Augmented Q&A System using Scalable Vision Embedding Retrieval & Late Interaction Re-rankerCode0
Leveraging RAG-LLMs for Urban Mobility Simulation and Analysis—0
MIRIX: Multi-Agent Memory System for LLM-Based Agents—0
Orchestrator-Agent Trust: A Modular Agentic AI Visual Classification System with Trust-Aware Orchestration and RAG-Based ReasoningCode0
Multi-Agent Retrieval-Augmented Framework for Evidence-Based Counterspeech Against Health Misinformation—0
The Dark Side of LLMs Agent-based Attacks for Complete Computer Takeover—0
CLI-RAG: A Retrieval-Augmented Framework for Clinically Structured and Context Aware Text Generation with LLMs—0
SARA: Selective and Adaptive Retrieval-augmented Generation with Context Compression—0
Flippi: End To End GenAI Assistant for E-Commerce—0
Evaluating Memory in LLM Agents via Incremental Multi-Turn Interactions—0
AI-VaxGuide: An Agentic RAG-Based LLM for Vaccination Decisions—0
CyberRAG: An agentic RAG cyber attack classification and reporting tool—0
Knowledge Protocol Engineering: A New Paradigm for AI in Domain-Specific Knowledge Work—0
RAG-R1 : Incentivize the Search and Reasoning Capabilities of LLMs through Multi-query ParallelismCode5
Knowledge Augmented Finetuning Matters in both RAG and Agent Based Dialog Systems—0
ARAG: Agentic Retrieval Augmented Generation for Personalized Recommendation—0
EraRAG: Efficient and Incremental Retrieval Augmented Generation for Growing CorporaCode2
Leveraging LLM-Assisted Query Understanding for Live Retrieval-Augmented Generation—0
Response Quality Assessment for Retrieval-Augmented Generation via Conditional Conformal FactualityCode0
PsyLite Technical ReportCode0
Engineering RAG Systems for Real-World Applications: Design, Development, and Evaluation—0
RAG-VisualRec: An Open Resource for Vision- and Text-Enhanced Retrieval-Augmented Generation in RecommendationCode0
MultiFinRAG: An Optimized Multimodal Retrieval-Augmented Generation (RAG) Framework for Financial Question Answering—0
Knowledge-Aware Diverse Reranking for Cross-Source Question Answering—0
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