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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 1551–1575 of 2111 papers

TitleStatusHype
From Feature Importance to Natural Language Explanations Using LLMs with RAGCode0
Introducing a new hyper-parameter for RAG: Context Window Utilization—0
A Study on the Implementation Method of an Agent-Based Advanced RAG System Using Graph—0
Enhancing Code Translation in Language Models with Few-Shot Learning via Retrieval-Augmented Generation—0
Faculty Perspectives on the Potential of RAG in Computer Science Higher Education—0
ChipExpert: The Open-Source Integrated-Circuit-Design-Specific Large Language Model—0
Modular RAG: Transforming RAG Systems into LEGO-like Reconfigurable Frameworks—0
REAPER: Reasoning based Retrieval Planning for Complex RAG Systems—0
The Geometry of Queries: Query-Based Innovations in Retrieval-Augmented Generation—0
Bailicai: A Domain-Optimized Retrieval-Augmented Generation Framework for Medical Applications—0
LawLuo: A Multi-Agent Collaborative Framework for Multi-Round Chinese Legal Consultation—0
Retrieval Augmented Generation or Long-Context LLMs? A Comprehensive Study and Hybrid Approach—0
An Empirical Comparison of Video Frame Sampling Methods for Multi-Modal RAG Retrieval—0
KaPQA: Knowledge-Augmented Product Question-Answering—0
NV-Retriever: Improving text embedding models with effective hard-negative mining—0
RadioRAG: Factual large language models for enhanced diagnostics in radiology using online retrieval augmented generationCode0
An Empirical Study of Retrieval Augmented Generation with Chain-of-Thought—0
MoRSE: Bridging the Gap in Cybersecurity Expertise with Retrieval Augmented Generation—0
LLMmap: Fingerprinting For Large Language ModelsCode3
Customized Retrieval Augmented Generation and Benchmarking for EDA Tool Documentation QACode0
Decoding BACnet Packets: A Large Language Model Approach for Packet Interpretation—0
AutoVCoder: A Systematic Framework for Automated Verilog Code Generation using LLMs—0
Fact-Aware Multimodal Retrieval Augmentation for Accurate Medical Radiology Report Generation—0
Golden-Retriever: High-Fidelity Agentic Retrieval Augmented Generation for Industrial Knowledge Base—0
Automatic Generation of Fashion Images using Prompting in Generative Machine Learning ModelsCode0
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