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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 101–150 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
Adversarial Threat Vectors and Risk Mitigation for Retrieval-Augmented Generation Systems—0
An AI-powered Knowledge Hub for Potato Functional Genomics—0
ClueAnchor: Clue-Anchored Knowledge Reasoning Exploration and Optimization for Retrieval-Augmented GenerationCode0
RealDrive: Retrieval-Augmented Driving with Diffusion Models—0
Guiding Generative Storytelling with Knowledge Graphs—0
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
Query Routing for Retrieval-Augmented Language Models—0
Data-efficient Meta-models for Evaluation of Context-based Questions and Answers in LLMs—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
DocReRank: Single-Page Hard Negative Query Generation for Training Multi-Modal RAG Rerankers—0
Contextual Memory Intelligence -- A Foundational Paradigm for Human-AI Collaboration and Reflective Generative AI Systems—0
RAG-Zeval: Towards Robust and Interpretable Evaluation on RAG Responses through End-to-End Rule-Guided Reasoning—0
VRAG-RL: Empower Vision-Perception-Based RAG for Visually Rich Information Understanding via Iterative Reasoning with Reinforcement LearningCode3
SkewRoute: Training-Free LLM Routing for Knowledge Graph Retrieval-Augmented Generation via Score Skewness of Retrieved Context—0
RAGPPI: RAG Benchmark for Protein-Protein Interactions in Drug DiscoveryCode0
Cross-modal RAG: Sub-dimensional Retrieval-Augmented Text-to-Image GenerationCode0
Climate Finance BenchCode0
Agent-UniRAG: A Trainable Open-Source LLM Agent Framework for Unified Retrieval-Augmented Generation Systems—0
DORAEMON: Decentralized Ontology-aware Reliable Agent with Enhanced Memory Oriented Navigation—0
Towards Efficient Key-Value Cache Management for Prefix Prefilling in LLM Inference—0
Long Context Scaling: Divide and Conquer via Multi-Agent Question-driven Collaboration—0
Rethinking Chunk Size For Long-Document Retrieval: A Multi-Dataset AnalysisCode0
What LLMs Miss in Recommendations: Bridging the Gap with Retrieval-Augmented Collaborative Signals—0
Diagnosing and Resolving Cloud Platform Instability with Multi-modal RAG LLMs—0
Public Discourse Sandbox: Facilitating Human and AI Digital Communication Research—0
AI-Supported Platform for System Monitoring and Decision-Making in Nuclear Waste Management with Large Language Models—0
Complex System Diagnostics Using a Knowledge Graph-Informed and Large Language Model-Enhanced Framework—0
LLM Web Dynamics: Tracing Model Collapse in a Network of LLMs—0
Project Riley: Multimodal Multi-Agent LLM Collaboration with Emotional Reasoning and Voting—0
R3-RAG: Learning Step-by-Step Reasoning and Retrieval for LLMs via Reinforcement LearningCode1
Benchmarking Multimodal Knowledge Conflict for Large Multimodal ModelsCode1
syftr: Pareto-Optimal Generative AICode3
Anveshana: A New Benchmark Dataset for Cross-Lingual Information Retrieval On English Queries and Sanskrit Documents—0
NeuSym-RAG: Hybrid Neural Symbolic Retrieval with Multiview Structuring for PDF Question AnsweringCode1
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