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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 1201–1250 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
A review of faithfulness metrics for hallucination assessment in Large Language Models—0
MAIN-RAG: Multi-Agent Filtering Retrieval-Augmented Generation—0
EdgeRAG: Online-Indexed RAG for Edge Devices—0
Retrieval-Augmented Generation for Mobile Edge Computing via Large Language Model—0
TimeRAF: Retrieval-Augmented Foundation model for Zero-shot Time Series Forecasting—0
Enhanced Multimodal RAG-LLM for Accurate Visual Question Answering—0
A Comprehensive Framework for Reliable Legal AI: Combining Specialized Expert Systems and Adaptive Refinement—0
Understanding the Impact of Confidence in Retrieval Augmented Generation: A Case Study in the Medical DomainCode0
From Interests to Insights: An LLM Approach to Course Recommendations Using Natural Language QueriesCode0
GeAR: Graph-enhanced Agent for Retrieval-augmented Generation—0
DynaGRAG | Exploring the Topology of Information for Advancing Language Understanding and Generation in Graph Retrieval-Augmented Generation—0
Molly: Making Large Language Model Agents Solve Python Problem More Logically—0
Pirates of the RAG: Adaptively Attacking LLMs to Leak Knowledge Bases—0
Improving Factuality with Explicit Working Memory—0
EvoPat: A Multi-LLM-based Patents Summarization and Analysis Agent—0
A Survey of Query Optimization in Large Language Models—0
RAGONITE: Iterative Retrieval on Induced Databases and Verbalized RDF for Conversational QA over KGs with RAG—0
Contrato360 2.0: A Document and Database-Driven Question-Answer System using Large Language Models and Agents—0
Correctness is not Faithfulness in RAG Attributions—0
Dynamic Multi-Agent Orchestration and Retrieval for Multi-Source Question-Answer Systems using Large Language Models—0
The HalluRAG Dataset: Detecting Closed-Domain Hallucinations in RAG Applications Using an LLM's Internal StatesCode0
LLM Agent for Fire Dynamics Simulations—0
A Reality Check on Context Utilisation for Retrieval-Augmented GenerationCode0
Speech Retrieval-Augmented Generation without Automatic Speech Recognition—0
AlzheimerRAG: Multimodal Retrieval Augmented Generation for PubMed articles—0
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