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Methods/RAG

RAG

Natural Language ProcessingIntroduced 20001286 papers
Source Paper

Description

Retriever-Augmented Generation, or RAG, is a type of language generation model that combines pre-trained parametric and non-parametric memory for language generation. Specifically, the parametric memory is a pre-trained seq2seq model and the non-parametric memory is a dense vector index of Wikipedia, accessed with a pre-trained neural retriever. For query xxx, Maximum Inner Product Search (MIPS) is used to find the top-K documents z_iz\_{i}z_i. For final prediction yyy, we treat zzz as a latent variable and marginalize over seq2seq predictions given different documents.

Papers Using This Method

Developing Visual Augmented Q&A System using Scalable Vision Embedding Retrieval & Late Interaction Re-ranker2025-07-16Leveraging RAG-LLMs for Urban Mobility Simulation and Analysis2025-07-14The Dark Side of LLMs Agent-based Attacks for Complete Computer Takeover2025-07-09Orchestrator-Agent Trust: A Modular Agentic AI Visual Classification System with Trust-Aware Orchestration and RAG-Based Reasoning2025-07-09SARA: Selective and Adaptive Retrieval-augmented Generation with Context Compression2025-07-08Knowledge Protocol Engineering: A New Paradigm for AI in Domain-Specific Knowledge Work2025-07-03CyberRAG: An agentic RAG cyber attack classification and reporting tool2025-07-03Knowledge Augmented Finetuning Matters in both RAG and Agent Based Dialog Systems2025-06-28ARAG: Agentic Retrieval Augmented Generation for Personalized Recommendation2025-06-27Response Quality Assessment for Retrieval-Augmented Generation via Conditional Conformal Factuality2025-06-26PsyLite Technical Report2025-06-26EraRAG: Efficient and Incremental Retrieval Augmented Generation for Growing Corpora2025-06-26Leveraging LLM-Assisted Query Understanding for Live Retrieval-Augmented Generation2025-06-26AI Assistants to Enhance and Exploit the PETSc Knowledge Base2025-06-25CCRS: A Zero-Shot LLM-as-a-Judge Framework for Comprehensive RAG Evaluation2025-06-25Knowledge-Aware Diverse Reranking for Cross-Source Question Answering2025-06-25Memento: Note-Taking for Your Future Self2025-06-25Engineering RAG Systems for Real-World Applications: Design, Development, and Evaluation2025-06-25Accurate and Energy Efficient: Local Retrieval-Augmented Generation Models Outperform Commercial Large Language Models in Medical Tasks2025-06-24Inference Scaled GraphRAG: Improving Multi Hop Question Answering on Knowledge Graphs2025-06-24