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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 101–125 of 2111 papers

TitleStatusHype
Retrieval-Augmented Generation as Noisy In-Context Learning: A Unified Theory and Risk Bounds—0
MotionRAG-Diff: A Retrieval-Augmented Diffusion Framework for Long-Term Music-to-Dance Generation—0
Hybrid AI for Responsive Multi-Turn Online Conversations with Novel Dynamic Routing and Feedback Adaptation—0
LLMs as World Models: Data-Driven and Human-Centered Pre-Event Simulation for Disaster Impact Assessment—0
Retrieval-Augmented Generation of Ontologies from Relational Databases—0
A Graph-Retrieval-Augmented Generation Framework Enhances Decision-Making in the Circular Economy—0
RARE: Retrieval-Aware Robustness Evaluation for Retrieval-Augmented Generation SystemsCode0
A Large Language Model-Supported Threat Modeling Framework for Transportation Cyber-Physical Systems—0
PAKTON: A Multi-Agent Framework for Question Answering in Long Legal AgreementsCode1
FinBERT2: A Specialized Bidirectional Encoder for Bridging the Gap in Finance-Specific Deployment of Large Language Models—0
An AI-powered Knowledge Hub for Potato Functional Genomics—0
Adversarial Threat Vectors and Risk Mitigation for Retrieval-Augmented Generation Systems—0
Guiding Generative Storytelling with Knowledge Graphs—0
RealDrive: Retrieval-Augmented Driving with Diffusion Models—0
ClueAnchor: Clue-Anchored Knowledge Reasoning Exploration and Optimization for Retrieval-Augmented GenerationCode0
E^2GraphRAG: Streamlining Graph-based RAG for High Efficiency and Effectiveness—0
Retrieval Augmented Generation based Large Language Models for Causality MiningCode0
mRAG: Elucidating the Design Space of Multi-modal Retrieval-Augmented Generation—0
Multi-RAG: A Multimodal Retrieval-Augmented Generation System for Adaptive Video Understanding—0
Diagnosing and Addressing Pitfalls in KG-RAG Datasets: Toward More Reliable Benchmarking—0
Data-efficient Meta-models for Evaluation of Context-based Questions and Answers in LLMs—0
Query Routing for Retrieval-Augmented Language Models—0
Sentinel: Attention Probing of Proxy Models for LLM Context Compression with an Understanding PerspectiveCode1
MCP Safety Training: Learning to Refuse Falsely Benign MCP Exploits using Improved Preference Alignment—0
MemOS: An Operating System for Memory-Augmented Generation (MAG) in Large Language Models—0
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