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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 1976–2000 of 2111 papers

TitleStatusHype
HEALTH-PARIKSHA: Assessing RAG Models for Health Chatbots in Real-World Multilingual Settings—0
HealthQ: Unveiling Questioning Capabilities of LLM Chains in Healthcare Conversations—0
HeteRAG: A Heterogeneous Retrieval-augmented Generation Framework with Decoupled Knowledge Representations—0
Hierarchical Planning for Complex Tasks with Knowledge Graph-RAG and Symbolic Verification—0
HijackRAG: Hijacking Attacks against Retrieval-Augmented Large Language Models—0
HiPerRAG: High-Performance Retrieval Augmented Generation for Scientific Insights—0
HiQA: A Hierarchical Contextual Augmentation RAG for Multi-Documents QA—0
HoH: A Dynamic Benchmark for Evaluating the Impact of Outdated Information on Retrieval-Augmented Generation—0
Holistic Reasoning with Long-Context LMs: A Benchmark for Database Operations on Massive Textual Data—0
Homa at SemEval-2025 Task 5: Aligning Librarian Records with OntoAligner for Subject Tagging—0
Honest AI: Fine-Tuning "Small" Language Models to Say "I Don't Know", and Reducing Hallucination in RAG—0
HopRAG: Multi-Hop Reasoning for Logic-Aware Retrieval-Augmented Generation—0
How Much Can RAG Help the Reasoning of LLM?—0
How to Build an AI Tutor That Can Adapt to Any Course Using Knowledge Graph-Enhanced Retrieval-Augmented Generation (KG-RAG)—0
Human-Calibrated Automated Testing and Validation of Generative Language Models—0
Human Cognition Inspired RAG with Knowledge Graph for Complex Problem Solving—0
Human-Imperceptible Retrieval Poisoning Attacks in LLM-Powered Applications—0
HybGRAG: Hybrid Retrieval-Augmented Generation on Textual and Relational Knowledge Bases—0
Hybrid AI for Responsive Multi-Turn Online Conversations with Novel Dynamic Routing and Feedback Adaptation—0
Hybrid RAG-empowered Multi-modal LLM for Secure Data Management in Internet of Medical Things: A Diffusion-based Contract Approach—0
HybridRAG: Integrating Knowledge Graphs and Vector Retrieval Augmented Generation for Efficient Information Extraction—0
Hybrid-SQuAD: Hybrid Scholarly Question Answering Dataset—0
Hybrid Student-Teacher Large Language Model Refinement for Cancer Toxicity Symptom Extraction—0
HyPA-RAG: A Hybrid Parameter Adaptive Retrieval-Augmented Generation System for AI Legal and Policy Applications—0
Hyper-RAG: Combating LLM Hallucinations using Hypergraph-Driven Retrieval-Augmented Generation—0
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