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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 451–500 of 2111 papers

TitleStatusHype
From RAG to QA-RAG: Integrating Generative AI for Pharmaceutical Regulatory Compliance ProcessCode1
Advancing TTP Analysis: Harnessing the Power of Large Language Models with Retrieval Augmented GenerationCode1
HyKGE: A Hypothesis Knowledge Graph Enhanced Framework for Accurate and Reliable Medical LLMs ResponsesCode1
Context-aware Decoding Reduces Hallucination in Query-focused SummarizationCode1
Dynamic Retrieval Augmented Generation of Ontologies using Artificial Intelligence (DRAGON-AI)Code1
"Knowing When You Don't Know": A Multilingual Relevance Assessment Dataset for Robust Retrieval-Augmented GenerationCode1
Qilin-Med: Multi-stage Knowledge Injection Advanced Medical Large Language ModelCode1
Glitter or Gold? Deriving Structured Insights from Sustainability Reports via Large Language ModelsCode1
Retrieval-augmented Generation to Improve Math Question-Answering: Trade-offs Between Groundedness and Human PreferenceCode1
Exploring Parameter-Efficient Fine-Tuning Techniques for Code Generation with Large Language ModelsCode1
Deep Equilibrium Object DetectionCode1
Retrieval Augmented Generation and Representative Vector Summarization for large unstructured textual data in Medical EducationCode1
Merging-Diverging Hybrid Transformer Networks for Survival Prediction in Head and Neck CancerCode1
Re2G: Retrieve, Rerank, GenerateCode1
Zero-shot Slot Filling with DPR and RAGCode1
End-to-End Training of Neural Retrievers for Open-Domain Question AnsweringCode1
Two-Stage Single Image Reflection Removal with Reflection-Aware GuidanceCode1
Superpixel Image Classification with Graph Attention NetworksCode1
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
CLI-RAG: A Retrieval-Augmented Framework for Clinically Structured and Context Aware Text Generation with LLMs—0
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
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
Knowledge Protocol Engineering: A New Paradigm for AI in Domain-Specific Knowledge Work—0
CyberRAG: An agentic RAG cyber attack classification and reporting tool—0
Knowledge Augmented Finetuning Matters in both RAG and Agent Based Dialog Systems—0
ARAG: Agentic Retrieval Augmented Generation for Personalized Recommendation—0
Leveraging LLM-Assisted Query Understanding for Live Retrieval-Augmented Generation—0
PsyLite Technical ReportCode0
Response Quality Assessment for Retrieval-Augmented Generation via Conditional Conformal FactualityCode0
Fine-Tuning and Prompt Engineering of LLMs, for the Creation of Multi-Agent AI for Addressing Sustainable Protein Production ChallengesCode0
AI Assistants to Enhance and Exploit the PETSc Knowledge Base—0
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
CCRS: A Zero-Shot LLM-as-a-Judge Framework for Comprehensive RAG Evaluation—0
MultiFinRAG: An Optimized Multimodal Retrieval-Augmented Generation (RAG) Framework for Financial Question Answering—0
SV-LLM: An Agentic Approach for SoC Security Verification using Large Language Models—0
Knowledge-Aware Diverse Reranking for Cross-Source Question Answering—0
Memento: Note-Taking for Your Future Self—0
Controlled Retrieval-augmented Context Evaluation for Long-form RAG—0
Accurate and Energy Efficient: Local Retrieval-Augmented Generation Models Outperform Commercial Large Language Models in Medical Tasks—0
Dialogic Pedagogy for Large Language Models: Aligning Conversational AI with Proven Theories of Learning—0
QHackBench: Benchmarking Large Language Models for Quantum Code Generation Using PennyLane Hackathon Challenges—0
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