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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 451–475 of 2111 papers

TitleStatusHype
Don't Lag, RAG: Training-Free Adversarial Detection Using RAG—0
Driving-RAG: Driving Scenarios Embedding, Search, and RAG Applications—0
Hierarchical Planning for Complex Tasks with Knowledge Graph-RAG and Symbolic Verification—0
QE-RAG: A Robust Retrieval-Augmented Generation Benchmark for Query Entry Errors—0
NAACL2025 Tutorial: Adaptation of Large Language Models—0
Generative AI Enhanced Financial Risk Management Information RetrievalCode0
Efficient Dynamic Clustering-Based Document Compression for Retrieval-Augmented-GenerationCode1
Rotation Invariance in Floor Plan Digitization using Zernike Moments—0
DeepResearcher: Scaling Deep Research via Reinforcement Learning in Real-world EnvironmentsCode4
Practical Poisoning Attacks against Retrieval-Augmented Generation—0
Multilingual Retrieval-Augmented Generation for Knowledge-Intensive Task—0
Talk2X -- An Open-Source Toolkit Facilitating Deployment of LLM-Powered Chatbots on the WebCode0
Do "New Snow Tablets" Contain Snow? Large Language Models Over-Rely on Names to Identify Ingredients of Chinese DrugsCode0
HyperRAG: Enhancing Quality-Efficiency Tradeoffs in Retrieval-Augmented Generation with Reranker KV-Cache Reuse—0
Retrieval-Augmented Purifier for Robust LLM-Empowered Recommendation—0
Affordable AI Assistants with Knowledge Graph of ThoughtsCode3
Adapting Large Language Models for Multi-Domain Retrieval-Augmented-Generation—0
OnRL-RAG: Real-Time Personalized Mental Health Dialogue System—0
One Pic is All it Takes: Poisoning Visual Document Retrieval Augmented Generation with a Single Image—0
GeoRAG: A Question-Answering Approach from a Geographical Perspective—0
CoRAG: Collaborative Retrieval-Augmented Generation—0
LARGE: Legal Retrieval Augmented Generation Evaluation ToolCode2
Reasoning LLMs for User-Aware Multimodal Conversational Agents—0
From Code Generation to Software Testing: AI Copilot with Context-Based RAG—0
GTR: Graph-Table-RAG for Cross-Table Question Answering—0
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