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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
RAG-WM: An Efficient Black-Box Watermarking Approach for Retrieval-Augmented Generation of Large Language Models—0
A General Retrieval-Augmented Generation Framework for Multimodal Case-Based Reasoning Applications—0
Biomedical Relation Extraction via Adaptive Document-Relation Cross-Mapping and Concept Unique Identifier—0
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
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
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
Reading with Intent -- Neutralizing Intent—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
Sustainable Digitalization of Business with Multi-Agent RAG and LLM—0
Political Events using RAG with LLMs—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
The Efficiency vs. Accuracy Trade-off: Optimizing RAG-Enhanced LLM Recommender Systems Using Multi-Head Early Exit—0
Knowledge Graph Retrieval-Augmented Generation for LLM-based Recommendation—0
PersonaAI: Leveraging Retrieval-Augmented Generation and Personalized Context for AI-Driven Digital Avatars—0
Are LLMs effective psychological assessors? Leveraging adaptive RAG for interpretable mental health screening through psychometric practiceCode0
ValuesRAG: Enhancing Cultural Alignment Through Retrieval-Augmented Contextual Learning—0
Docopilot: Improving Multimodal Models for Document-Level UnderstandingCode1
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
TrustRAG: Enhancing Robustness and Trustworthiness in RAGCode2
Decoding the Flow: CauseMotion for Emotional Causality Analysis in Long-form Conversations—0
RAG-Instruct: Boosting LLMs with Diverse Retrieval-Augmented InstructionsCode2
A review of faithfulness metrics for hallucination assessment in Large Language Models—0
MAIN-RAG: Multi-Agent Filtering Retrieval-Augmented Generation—0
CancerKG.ORG A Web-scale, Interactive, Verifiable Knowledge Graph-LLM Hybrid for Assisting with Optimal Cancer Treatment and Care—0
Retrieval-Augmented Generation with Graphs (GraphRAG)Code0
Retrieval-Augmented Generation for Mobile Edge Computing via Large Language Model—0
GASLITEing the Retrieval: Exploring Vulnerabilities in Dense Embedding-based SearchCode1
TimeRAF: Retrieval-Augmented Foundation model for Zero-shot Time Series Forecasting—0
EdgeRAG: Online-Indexed RAG for Edge Devices—0
Enhanced Multimodal RAG-LLM for Accurate Visual Question Answering—0
Plancraft: an evaluation dataset for planning with LLM agentsCode1
Understanding the Impact of Confidence in Retrieval Augmented Generation: A Case Study in the Medical DomainCode0
A Comprehensive Framework for Reliable Legal AI: Combining Specialized Expert Systems and Adaptive Refinement—0
Long Context vs. RAG for LLMs: An Evaluation and RevisitsCode1
Jasper and Stella: distillation of SOTA embedding modelsCode1
RAG with Differential PrivacyCode1
From Interests to Insights: An LLM Approach to Course Recommendations Using Natural Language QueriesCode0
DynaGRAG | Exploring the Topology of Information for Advancing Language Understanding and Generation in Graph Retrieval-Augmented Generation—0
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