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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
Good/Evil Reputation Judgment of Celebrities by LLMs via Retrieval Augmented Generation—0
RAD: Retrieval-Augmented Decision-Making of Meta-Actions with Vision-Language Models in Autonomous Driving—0
Beyond Single Pass, Looping Through Time: KG-IRAG with Iterative Knowledge Retrieval—0
RAG-RL: Advancing Retrieval-Augmented Generation via RL and Curriculum Learning—0
OSCAR: Online Soft Compression And Reranking—0
Privacy-Aware RAG: Secure and Isolated Knowledge Retrieval—0
MES-RAG: Bringing Multi-modal, Entity-Storage, and Secure Enhancements to RAGCode0
LLM-Match: An Open-Sourced Patient Matching Model Based on Large Language Models and Retrieval-Augmented Generation—0
Generative AI for Software Architecture. Applications, Trends, Challenges, and Future Directions—0
Logic-RAG: Augmenting Large Multimodal Models with Visual-Spatial Knowledge for Road Scene UnderstandingCode1
Datrics Text2SQL. A Framework for Natural Language to SQL Query GenerationCode2
Integrating Chain-of-Thought and Retrieval Augmented Generation Enhances Rare Disease Diagnosis from Clinical Notes—0
Agentic Search Engine for Real-Time IoT DataCode0
TFHE-Coder: Evaluating LLM-agentic Fully Homomorphic Encryption Code Generation—0
Relevance Isn't All You Need: Scaling RAG Systems With Inference-Time Compute Via Multi-Criteria RerankingCode14
RAG-KG-IL: A Multi-Agent Hybrid Framework for Reducing Hallucinations and Enhancing LLM Reasoning through RAG and Incremental Knowledge Graph Learning Integration—0
Agent-Enhanced Large Language Models for Researching Political InstitutionsCode0
MUSS: Multilevel Subset Selection for Relevance and Diversity—0
AIstorian lets AI be a historian: A KG-powered multi-agent system for accurate biography generationCode0
FG-RAG: Enhancing Query-Focused Summarization with Context-Aware Fine-Grained Graph RAGCode0
AttentionRAG: Attention-Guided Context Pruning in Retrieval-Augmented Generation—0
Taxonomic Reasoning for Rare Arthropods: Combining Dense Image Captioning and RAG for Interpretable Classification—0
SurgRAW: Multi-Agent Workflow with Chain-of-Thought Reasoning for Surgical Intelligence—0
Retrieval-Augmented Generation with Hierarchical KnowledgeCode4
ClaimTrust: Propagation Trust Scoring for RAG Systems—0
Conversational Gold: Evaluating Personalized Conversational Search System using Gold NuggetsCode0
CALLM: Understanding Cancer Survivors' Emotions and Intervention Opportunities via Mobile Diaries and Context-Aware Language Models—0
Search-R1: Training LLMs to Reason and Leverage Search Engines with Reinforcement LearningCode7
Everything Can Be Described in Words: A Simple Unified Multi-Modal Framework with Semantic and Temporal Alignment—0
MoC: Mixtures of Text Chunking Learners for Retrieval-Augmented Generation SystemCode3
Memory-enhanced Retrieval Augmentation for Long Video Understanding—0
How to Protect Yourself from 5G Radiation? Investigating LLM Responses to Implicit MisinformationCode0
OpenRAG: Optimizing RAG End-to-End via In-Context Retrieval Learning—0
A Survey on Knowledge-Oriented Retrieval-Augmented Generation—0
Training Plug-n-Play Knowledge Modules with Deep Context Distillation—0
Privacy-Enhancing Paradigms within Federated Multi-Agent SystemsCode0
LLM-based Corroborating and Refuting Evidence Retrieval for Scientific Claim Verification—0
Enhancing Retrieval for ESGLLM via ESG-CID -- A Disclosure Content Index Finetuning Dataset for Mapping GRI and ESRS—0
Advancing Vietnamese Information Retrieval with Learning Objective and Benchmark—0
Talking to GDELT Through Knowledge Graphs—0
CtrlRAG: Black-box Adversarial Attacks Based on Masked Language Models in Retrieval-Augmented Language Generation—0
LLM-C3MOD: A Human-LLM Collaborative System for Cross-Cultural Hate Speech Moderation—0
Human Cognition Inspired RAG with Knowledge Graph for Complex Problem Solving—0
Poisoned-MRAG: Knowledge Poisoning Attacks to Multimodal Retrieval Augmented Generation—0
Interpersonal Memory Matters: A New Task for Proactive Dialogue Utilizing Conversational HistoryCode0
Improving RAG Retrieval via Propositional Content Extraction: a Speech Act Theory Approach—0
Quantifying the Robustness of Retrieval-Augmented Language Models Against Spurious Features in Grounding DataCode0
Personalized Text Generation with Contrastive Activation Steering—0
R1-Searcher: Incentivizing the Search Capability in LLMs via Reinforcement LearningCode4
FinTMMBench: Benchmarking Temporal-Aware Multi-Modal RAG in Finance—0
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