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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 1151–1175 of 2111 papers

TitleStatusHype
IntellBot: Retrieval Augmented LLM Chatbot for Cyber Threat Knowledge DeliveryCode0
Qwen2.5-32B: Leveraging Self-Consistent Tool-Integrated Reasoning for Bengali Mathematical Olympiad Problem Solving—0
FinDVer: Explainable Claim Verification over Long and Hybrid-Content Financial DocumentsCode0
Enhancing Cluster Resilience: LLM-agent Based Autonomous Intelligent Cluster Diagnosis System and Evaluation Framework—0
AgentOps: Enabling Observability of LLM Agents—0
AMSnet-KG: A Netlist Dataset for LLM-based AMS Circuit Auto-Design Using Knowledge Graph RAG—0
Deploying Large Language Models With Retrieval Augmented GenerationCode0
Audiobox TTA-RAG: Improving Zero-Shot and Few-Shot Text-To-Audio with Retrieval-Augmented Generation—0
LLM-R: A Framework for Domain-Adaptive Maintenance Scheme Generation Combining Hierarchical Agents and RAG—0
ML-Promise: A Multilingual Dataset for Corporate Promise Verification—0
M3DocRAG: Multi-modal Retrieval is What You Need for Multi-page Multi-document Understanding—0
Enhancing classroom teaching with LLMs and RAG—0
LEGO-GraphRAG: Modularizing Graph-based Retrieval-Augmented Generation for Design Space Exploration—0
Advanced RAG Models with Graph Structures: Optimizing Complex Knowledge Reasoning and Text Generation—0
RAGulator: Lightweight Out-of-Context Detectors for Grounded Text Generation—0
Fine-Grained Guidance for Retrievers: Leveraging LLMs' Feedback in Retrieval-Augmented Generation—0
Long Context RAG Performance of Large Language Models—0
PersianRAG: A Retrieval-Augmented Generation System for Persian Language—0
WASHtsApp -- A RAG-powered WhatsApp Chatbot for supporting rural African clean water access, sanitation and hygiene—0
HtmlRAG: HTML is Better Than Plain Text for Modeling Retrieved Knowledge in RAG SystemsCode3
RuAG: Learned-rule-augmented Generation for Large Language Models—0
TeleOracle: Fine-Tuned Retrieval-Augmented Generation with Long-Context Support for NetworkCode1
Zebra-Llama: A Context-Aware Large Language Model for Democratizing Rare Disease KnowledgeCode1
RAGViz: Diagnose and Visualize Retrieval-Augmented GenerationCode2
Can Language Models Enable In-Context Database?—0
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