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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 901–925 of 2111 papers

TitleStatusHype
Search-o1: Agentic Search-Enhanced Large Reasoning ModelsCode5
Biomedical Relation Extraction via Adaptive Document-Relation Cross-Mapping and Concept Unique Identifier—0
RAG-WM: An Efficient Black-Box Watermarking Approach for Retrieval-Augmented Generation of Large Language Models—0
Advancing Retrieval-Augmented Generation for Persian: Development of Language Models, Comprehensive Benchmarks, and Best Practices for Optimization—0
Multi-task retriever fine-tuning for domain-specific and efficient RAG—0
How Large is the Universe of RNA-Like Motifs? A Clustering Analysis of RNA Graph Motifs Using Topological DescriptorsCode0
End-to-End Bangla AI for Solving Math Olympiad Problem Benchmark: Leveraging Large Language Model Using Integrated Approach—0
Re-ranking the Context for Multimodal Retrieval Augmented Generation—0
Knowledge Retrieval Based on Generative AI—0
Practical Design and Benchmarking of Generative AI Applications for Surgical Billing and Coding—0
Reading with Intent -- Neutralizing Intent—0
MTRAG: A Multi-Turn Conversational Benchmark for Evaluating Retrieval-Augmented Generation SystemsCode2
SenseRAG: Constructing Environmental Knowledge Bases with Proactive Querying for LLM-Based Autonomous Driving—0
RAG-Check: Evaluating Multimodal Retrieval Augmented Generation Performance—0
Developing an Artificial Intelligence Tool for Personalized Breast Cancer Treatment Plans based on the NCCN Guidelines—0
Political Events using RAG with LLMs—0
Sustainable Digitalization of Business with Multi-Agent RAG and LLM—0
FlippedRAG: Black-Box Opinion Manipulation Adversarial Attacks to Retrieval-Augmented Generation Models—0
Tree-based RAG-Agent Recommendation System: A Case Study in Medical Test Data—0
QuIM-RAG: Advancing Retrieval-Augmented Generation with Inverted Question Matching for Enhanced QA Performance—0
LatteReview: A Multi-Agent Framework for Systematic Review Automation Using Large Language ModelsCode2
Towards Omni-RAG: Comprehensive Retrieval-Augmented Generation for Large Language Models in Medical Applications—0
Knowledge Graph Retrieval-Augmented Generation for LLM-based Recommendation—0
The Efficiency vs. Accuracy Trade-off: Optimizing RAG-Enhanced LLM Recommender Systems Using Multi-Head Early Exit—0
PersonaAI: Leveraging Retrieval-Augmented Generation and Personalized Context for AI-Driven Digital Avatars—0
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