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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 901–950 of 2111 papers

TitleStatusHype
Experience Retrieval-Augmentation with Electronic Health Records Enables Accurate Discharge QACode0
MedPlan:A Two-Stage RAG-Based System for Personalized Medical Plan Generation—0
RustEvo^2: An Evolving Benchmark for API Evolution in LLM-based Rust Code GenerationCode0
An LLM-Powered Clinical Calculator Chatbot Backed by Verifiable Clinical Calculators and their Metadata—0
Autonomous Radiotherapy Treatment Planning Using DOLA: A Privacy-Preserving, LLM-Based Optimization Agent—0
FutureGen: LLM-RAG Approach to Generate the Future Work of Scientific ArticleCode0
Typed-RAG: Type-aware Multi-Aspect Decomposition for Non-Factoid Question AnsweringCode0
Financial Analysis: Intelligent Financial Data Analysis System Based on LLM-RAG—0
Towards Lighter and Robust Evaluation for Retrieval Augmented GenerationCode0
Graph-Based Re-ranking: Emerging Techniques, Limitations, and Opportunities—0
Bias Evaluation and Mitigation in Retrieval-Augmented Medical Question-Answering Systems—0
Enhancing Pancreatic Cancer Staging with Large Language Models: The Role of Retrieval-Augmented Generation—0
RAG-based User Profiling for Precision Planning in Mixed-precision Over-the-Air Federated Learning—0
When Pigs Get Sick: Multi-Agent AI for Swine Disease Detection—0
Does Context Matter? ContextualJudgeBench for Evaluating LLM-based Judges in Contextual SettingsCode0
Beyond Single Pass, Looping Through Time: KG-IRAG with Iterative Knowledge Retrieval—0
RAD: Retrieval-Augmented Decision-Making of Meta-Actions with Vision-Language Models in Autonomous Driving—0
Good/Evil Reputation Judgment of Celebrities by LLMs via Retrieval Augmented Generation—0
From "Hallucination" to "Suture": Insights from Language Philosophy to Enhance Large Language Models—0
MoK-RAG: Mixture of Knowledge Paths Enhanced Retrieval-Augmented Generation for Embodied AI Environments—0
Enhancing LLM Generation with Knowledge Hypergraph for Evidence-Based Medicine—0
Privacy-Aware RAG: Secure and Isolated Knowledge Retrieval—0
Generative AI for Software Architecture. Applications, Trends, Challenges, and Future Directions—0
RAG-RL: Advancing Retrieval-Augmented Generation via RL and Curriculum Learning—0
LLM-Match: An Open-Sourced Patient Matching Model Based on Large Language Models and Retrieval-Augmented Generation—0
MES-RAG: Bringing Multi-modal, Entity-Storage, and Secure Enhancements to RAGCode0
OSCAR: Online Soft Compression And Reranking—0
Agentic Search Engine for Real-Time IoT DataCode0
TFHE-Coder: Evaluating LLM-agentic Fully Homomorphic Encryption Code Generation—0
Integrating Chain-of-Thought and Retrieval Augmented Generation Enhances Rare Disease Diagnosis from Clinical Notes—0
RAG-KG-IL: A Multi-Agent Hybrid Framework for Reducing Hallucinations and Enhancing LLM Reasoning through RAG and Incremental Knowledge Graph Learning Integration—0
MUSS: Multilevel Subset Selection for Relevance and Diversity—0
Agent-Enhanced Large Language Models for Researching Political InstitutionsCode0
AIstorian lets AI be a historian: A KG-powered multi-agent system for accurate biography generationCode0
AttentionRAG: Attention-Guided Context Pruning in Retrieval-Augmented Generation—0
FG-RAG: Enhancing Query-Focused Summarization with Context-Aware Fine-Grained Graph RAGCode0
SurgRAW: Multi-Agent Workflow with Chain-of-Thought Reasoning for Surgical Intelligence—0
Taxonomic Reasoning for Rare Arthropods: Combining Dense Image Captioning and RAG for Interpretable Classification—0
Conversational Gold: Evaluating Personalized Conversational Search System using Gold NuggetsCode0
ClaimTrust: Propagation Trust Scoring for RAG Systems—0
Everything Can Be Described in Words: A Simple Unified Multi-Modal Framework with Semantic and Temporal Alignment—0
Memory-enhanced Retrieval Augmentation for Long Video Understanding—0
CALLM: Understanding Cancer Survivors' Emotions and Intervention Opportunities via Mobile Diaries and Context-Aware Language Models—0
How to Protect Yourself from 5G Radiation? Investigating LLM Responses to Implicit MisinformationCode0
A Survey on Knowledge-Oriented Retrieval-Augmented Generation—0
Privacy-Enhancing Paradigms within Federated Multi-Agent SystemsCode0
Training Plug-n-Play Knowledge Modules with Deep Context Distillation—0
OpenRAG: Optimizing RAG End-to-End via In-Context Retrieval Learning—0
LLM-based Corroborating and Refuting Evidence Retrieval for Scientific Claim Verification—0
CtrlRAG: Black-box Adversarial Attacks Based on Masked Language Models in Retrieval-Augmented Language Generation—0
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