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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 1651–1675 of 2111 papers

TitleStatusHype
OG-RAG: Ontology-Grounded Retrieval-Augmented Generation For Large Language Models—0
OkraLong: A Flexible Retrieval-Augmented Framework for Long-Text Query Processing—0
OMGM: Orchestrate Multiple Granularities and Modalities for Efficient Multimodal Retrieval—0
OmniQuery: Contextually Augmenting Captured Multimodal Memory to Enable Personal Question Answering—0
On Automating Security Policies with Contemporary LLMs—0
On-Board Vision-Language Models for Personalized Autonomous Vehicle Motion Control: System Design and Real-World Validation—0
Onco-Retriever: Generative Classifier for Retrieval of EHR Records in Oncology—0
One Pic is All it Takes: Poisoning Visual Document Retrieval Augmented Generation with a Single Image—0
One Shot Dominance: Knowledge Poisoning Attack on Retrieval-Augmented Generation Systems—0
One-Shot Multilingual Font Generation Via ViT—0
OnRL-RAG: Real-Time Personalized Mental Health Dialogue System—0
On the Capacity of Citation Generation by Large Language Models—0
On the Feasibility of Using MultiModal LLMs to Execute AR Social Engineering Attacks—0
On the Role of Long-tail Knowledge in Retrieval Augmented Large Language Models—0
On the Vulnerability of Applying Retrieval-Augmented Generation within Knowledge-Intensive Application Domains—0
On the Way to LLM Personalization: Learning to Remember User Conversations—0
Ontology-Aware RAG for Improved Question-Answering in Cybersecurity Education—0
Open Foundation Models in Healthcare: Challenges, Paradoxes, and Opportunities with GenAI Driven Personalized Prescription—0
OpenRAG: Optimizing RAG End-to-End via In-Context Retrieval Learning—0
Open-Source Retrieval Augmented Generation Framework for Retrieving Accurate Medication Insights from Formularies for African Healthcare Workers—0
OpenThaiGPT 1.5: A Thai-Centric Open Source Large Language Model—0
Optimization of embeddings storage for RAG systems using quantization and dimensionality reduction techniques—0
Optimizing Knowledge Integration in Retrieval-Augmented Generation with Self-Selection—0
Optimizing Multi-Hop Document Retrieval Through Intermediate Representations—0
Optimizing open-domain question answering with graph-based retrieval augmented generation—0
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