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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 551–600 of 2111 papers

TitleStatusHype
Retrieval-Augmented Generation as Noisy In-Context Learning: A Unified Theory and Risk Bounds—0
Retrieval-Augmented Generation of Ontologies from Relational Databases—0
LLMs as World Models: Data-Driven and Human-Centered Pre-Event Simulation for Disaster Impact Assessment—0
Hybrid AI for Responsive Multi-Turn Online Conversations with Novel Dynamic Routing and Feedback Adaptation—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
FinBERT2: A Specialized Bidirectional Encoder for Bridging the Gap in Finance-Specific Deployment of Large Language Models—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
Adversarial Threat Vectors and Risk Mitigation for Retrieval-Augmented Generation Systems—0
An AI-powered Knowledge Hub for Potato Functional Genomics—0
E^2GraphRAG: Streamlining Graph-based RAG for High Efficiency and Effectiveness—0
Data-efficient Meta-models for Evaluation of Context-based Questions and Answers in LLMs—0
Multi-RAG: A Multimodal Retrieval-Augmented Generation System for Adaptive Video Understanding—0
Query Routing for Retrieval-Augmented Language Models—0
mRAG: Elucidating the Design Space of Multi-modal Retrieval-Augmented Generation—0
MCP Safety Training: Learning to Refuse Falsely Benign MCP Exploits using Improved Preference Alignment—0
Retrieval Augmented Generation based Large Language Models for Causality MiningCode0
Diagnosing and Addressing Pitfalls in KG-RAG Datasets: Toward More Reliable Benchmarking—0
Cross-modal RAG: Sub-dimensional Retrieval-Augmented Text-to-Image GenerationCode0
Contextual Memory Intelligence -- A Foundational Paradigm for Human-AI Collaboration and Reflective Generative AI Systems—0
Agent-UniRAG: A Trainable Open-Source LLM Agent Framework for Unified Retrieval-Augmented Generation Systems—0
SkewRoute: Training-Free LLM Routing for Knowledge Graph Retrieval-Augmented Generation via Score Skewness of Retrieved Context—0
RAG-Zeval: Towards Robust and Interpretable Evaluation on RAG Responses through End-to-End Rule-Guided Reasoning—0
Climate Finance BenchCode0
Towards Efficient Key-Value Cache Management for Prefix Prefilling in LLM Inference—0
MemOS: An Operating System for Memory-Augmented Generation (MAG) in Large Language Models—0
DORAEMON: Decentralized Ontology-aware Reliable Agent with Enhanced Memory Oriented Navigation—0
DocReRank: Single-Page Hard Negative Query Generation for Training Multi-Modal RAG Rerankers—0
RAGPPI: RAG Benchmark for Protein-Protein Interactions in Drug DiscoveryCode0
Public Discourse Sandbox: Facilitating Human and AI Digital Communication Research—0
What LLMs Miss in Recommendations: Bridging the Gap with Retrieval-Augmented Collaborative Signals—0
Complex System Diagnostics Using a Knowledge Graph-Informed and Large Language Model-Enhanced Framework—0
Rethinking Chunk Size For Long-Document Retrieval: A Multi-Dataset AnalysisCode0
Diagnosing and Resolving Cloud Platform Instability with Multi-modal RAG LLMs—0
Long Context Scaling: Divide and Conquer via Multi-Agent Question-driven Collaboration—0
AI-Supported Platform for System Monitoring and Decision-Making in Nuclear Waste Management with Large Language Models—0
Project Riley: Multimodal Multi-Agent LLM Collaboration with Emotional Reasoning and Voting—0
LLM Web Dynamics: Tracing Model Collapse in a Network of LLMs—0
Anveshana: A New Benchmark Dataset for Cross-Lingual Information Retrieval On English Queries and Sanskrit Documents—0
MA-RAG: Multi-Agent Retrieval-Augmented Generation via Collaborative Chain-of-Thought Reasoning—0
LLM-Agent-Controller: A Universal Multi-Agent Large Language Model System as a Control Engineer—0
DoctorRAG: Medical RAG Fusing Knowledge with Patient Analogy through Textual Gradients—0
DGRAG: Distributed Graph-based Retrieval-Augmented Generation in Edge-Cloud Systems—0
It's High Time: A Survey of Temporal Information Retrieval and Question Answering—0
Investigating Pedagogical Teacher and Student LLM Agents: Genetic Adaptation Meets Retrieval Augmented Generation Across Learning Style—0
Retrieval-Augmented Generation for Service Discovery: Chunking Strategies and Benchmarking—0
Hypercube-RAG: Hypercube-Based Retrieval-Augmented Generation for In-domain Scientific Question-AnsweringCode0
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