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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 451–500 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
Rotation Invariance in Floor Plan Digitization using Zernike Moments—0
DeepResearcher: Scaling Deep Research via Reinforcement Learning in Real-world EnvironmentsCode4
Multilingual Retrieval-Augmented Generation for Knowledge-Intensive Task—0
Practical Poisoning Attacks against Retrieval-Augmented Generation—0
Generative AI Enhanced Financial Risk Management Information RetrievalCode0
Efficient Dynamic Clustering-Based Document Compression for Retrieval-Augmented-GenerationCode1
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
CoRAG: Collaborative Retrieval-Augmented Generation—0
Reasoning LLMs for User-Aware Multimodal Conversational Agents—0
GeoRAG: A Question-Answering Approach from a Geographical Perspective—0
Scaling Test-Time Inference with Policy-Optimized, Dynamic Retrieval-Augmented Generation via KV Caching and Decoding—0
GTR: Graph-Table-RAG for Cross-Table Question Answering—0
LARGE: Legal Retrieval Augmented Generation Evaluation ToolCode2
Biomedical Question Answering via Multi-Level Summarization on a Local Knowledge Graph—0
PROPHET: An Inferable Future Forecasting Benchmark with Causal Intervened Likelihood EstimationCode0
From Code Generation to Software Testing: AI Copilot with Context-Based RAG—0
CyberBOT: Towards Reliable Cybersecurity Education via Ontology-Grounded Retrieval Augmented Generation—0
WikiVideo: Article Generation from Multiple VideosCode1
Accelerating Causal Network Discovery of Alzheimer Disease Biomarkers via Scientific Literature-based Retrieval Augmented Generation—0
AI Hiring with LLMs: A Context-Aware and Explainable Multi-Agent Framework for Resume Screening—0
Command A: An Enterprise-Ready Large Language Model—0
ScholarCopilot: Training Large Language Models for Academic Writing with Accurate Citations—0
AgentNet: Decentralized Evolutionary Coordination for LLM-based Multi-Agent Systems—0
Agentic Multimodal AI for Hyperpersonalized B2B and B2C Advertising in Competitive Markets: An AI-Driven Competitive Advertising Framework—0
Medical large language models are easily distractedCode0
LLM-Assisted Proactive Threat Intelligence for Automated Reasoning—0
Beyond Quacking: Deep Integration of Language Models and RAG into DuckDBCode3
UltraRAG: A Modular and Automated Toolkit for Adaptive Retrieval-Augmented GenerationCode9
InteractiveSurvey: An LLM-based Personalized and Interactive Survey Paper Generation SystemCode1
CrossFormer: Cross-Segment Semantic Fusion for Document Segmentation—0
Enhancing Large Language Models (LLMs) for Telecommunications using Knowledge Graphs and Retrieval-Augmented Generation—0
A Systematic Evaluation of LLM Strategies for Mental Health Text Analysis: Fine-tuning vs. Prompt Engineering vs. RAG—0
Dynamic Parametric Retrieval Augmented Generation for Test-time Knowledge EnhancementCode2
Hyper-RAG: Combating LLM Hallucinations using Hypergraph-Driven Retrieval-Augmented Generation—0
SCORE: Story Coherence and Retrieval Enhancement for AI Narratives—0
SalesRLAgent: A Reinforcement Learning Approach for Real-Time Sales Conversion Prediction and Optimization—0
GRASP: Municipal Budget AI Chatbots for Enhancing Civic Engagement—0
MHTS: Multi-Hop Tree Structure Framework for Generating Difficulty-Controllable QA Datasets for RAG Evaluation—0
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