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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 1201–1225 of 2111 papers

TitleStatusHype
Multi-task retriever fine-tuning for domain-specific and efficient RAG—0
Advancing Retrieval-Augmented Generation for Persian: Development of Language Models, Comprehensive Benchmarks, and Best Practices for Optimization—0
How Large is the Universe of RNA-Like Motifs? A Clustering Analysis of RNA Graph Motifs Using Topological DescriptorsCode0
Knowledge Retrieval Based on Generative AI—0
Reading with Intent -- Neutralizing Intent—0
SenseRAG: Constructing Environmental Knowledge Bases with Proactive Querying for LLM-Based Autonomous Driving—0
Practical Design and Benchmarking of Generative AI Applications for Surgical Billing and Coding—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
FlippedRAG: Black-Box Opinion Manipulation Adversarial Attacks to Retrieval-Augmented Generation Models—0
QuIM-RAG: Advancing Retrieval-Augmented Generation with Inverted Question Matching for Enhanced QA Performance—0
Political Events using RAG with LLMs—0
Tree-based RAG-Agent Recommendation System: A Case Study in Medical Test Data—0
Sustainable Digitalization of Business with Multi-Agent RAG and LLM—0
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
ValuesRAG: Enhancing Cultural Alignment Through Retrieval-Augmented Contextual Learning—0
Are LLMs effective psychological assessors? Leveraging adaptive RAG for interpretable mental health screening through psychometric practiceCode0
Beyond Words: AuralLLM and SignMST-C for Precise Sign Language Production and Bidirectional Accessibility—0
Beyond Text: Implementing Multimodal Large Language Model-Powered Multi-Agent Systems Using a No-Code Platform—0
Decoding the Flow: CauseMotion for Emotional Causality Analysis in Long-form Conversations—0
Retrieval-Augmented Generation with Graphs (GraphRAG)Code0
CancerKG.ORG A Web-scale, Interactive, Verifiable Knowledge Graph-LLM Hybrid for Assisting with Optimal Cancer Treatment and Care—0
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