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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 1251–1275 of 2111 papers

TitleStatusHype
TimeRAG: BOOSTING LLM Time Series Forecasting via Retrieval-Augmented Generation—0
Formal Language Knowledge Corpus for Retrieval Augmented Generation—0
Towards More Robust Retrieval-Augmented Generation: Evaluating RAG Under Adversarial Poisoning AttacksCode0
Large Language Model Can Be a Foundation for Hidden Rationale-Based RetrievalCode0
InfoTech Assistant : A Multimodal Conversational Agent for InfoTechnology Web Portal Queries—0
HybGRAG: Hybrid Retrieval-Augmented Generation on Textual and Relational Knowledge Bases—0
Knowledge Injection via Prompt Distillation—0
Query pipeline optimization for cancer patient question answering systems—0
Review-Then-Refine: A Dynamic Framework for Multi-Hop Question Answering with Temporal Adaptability—0
Dehallucinating Parallel Context Extension for Retrieval-Augmented Generation—0
DynamicKV: Task-Aware Adaptive KV Cache Compression for Long Context LLMs—0
A Retrieval-Augmented Generation Framework for Academic Literature Navigation in Data Science—0
SKETCH: Structured Knowledge Enhanced Text Comprehension for Holistic Retrieval—0
CORD: Balancing COnsistency and Rank Distillation for Robust Retrieval-Augmented Generation—0
Face the Facts! Evaluating RAG-based Fact-checking Pipelines in Realistic SettingsCode0
VISA: Retrieval Augmented Generation with Visual Source Attribution—0
Federated Learning and RAG Integration: A Scalable Approach for Medical Large Language Models—0
Language verY Rare for All—0
RAG for Effective Supply Chain Security Questionnaire Automation—0
Enhancing Rhetorical Figure Annotation: An Ontology-Based Web Application with RAG IntegrationCode0
Multi-OphthaLingua: A Multilingual Benchmark for Assessing and Debiasing LLM Ophthalmological QA in LMICs—0
EvoWiki: Evaluating LLMs on Evolving Knowledge—0
Chinese SafetyQA: A Safety Short-form Factuality Benchmark for Large Language Models—0
Adaptations of AI models for querying the LandMatrix database in natural languageCode0
RemoteRAG: A Privacy-Preserving LLM Cloud RAG Service—0
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