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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 1751–1775 of 2111 papers

TitleStatusHype
Query Routing for Retrieval-Augmented Language Models—0
Question-Answering Based Summarization of Electronic Health Records using Retrieval Augmented Generation—0
Question-Based Retrieval using Atomic Units for Enterprise RAG—0
QuIM-RAG: Advancing Retrieval-Augmented Generation with Inverted Question Matching for Enhanced QA Performance—0
QuOTE: Question-Oriented Text Embeddings—0
Qwen2.5-32B: Leveraging Self-Consistent Tool-Integrated Reasoning for Bengali Mathematical Olympiad Problem Solving—0
QwenLong-CPRS: Towards -LLMs with Dynamic Context Optimization—0
R^3AG: First Workshop on Refined and Reliable Retrieval Augmented Generation—0
R3 : Refined Retriever-Reader pipeline for Multidoc2dial—0
RAAD-LLM: Adaptive Anomaly Detection Using LLMs and RAG Integration—0
RAC3: Retrieval-Augmented Corner Case Comprehension for Autonomous Driving with Vision-Language Models—0
RAD: Retrieval-Augmented Decision-Making of Meta-Actions with Vision-Language Models in Autonomous Driving—0
RaFe: Ranking Feedback Improves Query Rewriting for RAG—0
RAG4ITOps: A Supervised Fine-Tunable and Comprehensive RAG Framework for IT Operations and Maintenance—0
RAG-6DPose: Retrieval-Augmented 6D Pose Estimation via Leveraging CAD as Knowledge Base—0
Rag and Roll: An End-to-End Evaluation of Indirect Prompt Manipulations in LLM-based Application Frameworks—0
RAGAR, Your Falsehood Radar: RAG-Augmented Reasoning for Political Fact-Checking using Multimodal Large Language Models—0
RAG-based Crowdsourcing Task Decomposition via Masked Contrastive Learning with Prompts—0
RAG-based Explainable Prediction of Road Users Behaviors for Automated Driving using Knowledge Graphs and Large Language Models—0
RAG based Question-Answering for Contextual Response Prediction System—0
RAG-based Question Answering over Heterogeneous Data and Text—0
RAG-based User Profiling for Precision Planning in Mixed-precision Over-the-Air Federated Learning—0
RAGBench: Explainable Benchmark for Retrieval-Augmented Generation Systems—0
RAGCache: Efficient Knowledge Caching for Retrieval-Augmented Generation—0
RAG-Check: Evaluating Multimodal Retrieval Augmented Generation Performance—0
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