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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 1076–1100 of 2111 papers

TitleStatusHype
Auto-RAG: Autonomous Retrieval-Augmented Generation for Large Language ModelsCode3
Know Your RAG: Dataset Taxonomy and Generation Strategies for Evaluating RAG Systems—0
Unimib Assistant: designing a student-friendly RAG-based chatbot for all their needs—0
Advanced System Integration: Analyzing OpenAPI Chunking for Retrieval-Augmented Generation—0
Knowledge Management for Automobile Failure Analysis Using Graph RAG—0
Towards Understanding Retrieval Accuracy and Prompt Quality in RAG Systems—0
RAGDiffusion: Faithful Cloth Generation via External Knowledge Assimilation—0
SIMS: Simulating Stylized Human-Scene Interactions with Retrieval-Augmented Script Generation—0
Efficient Learning Content Retrieval with Knowledge Injection—0
ICLERB: In-Context Learning Embedding and Reranker Benchmark—0
RevPRAG: Revealing Poisoning Attacks in Retrieval-Augmented Generation through LLM Activation Analysis—0
Habit Coach: Customising RAG-based chatbots to support behavior change—0
Evaluating and Improving the Robustness of Security Attack Detectors Generated by LLMsCode0
Automated Literature Review Using NLP Techniques and LLM-Based Retrieval-Augmented Generation—0
Path-RAG: Knowledge-Guided Key Region Retrieval for Open-ended Pathology Visual Question AnsweringCode2
AtomR: Atomic Operator-Empowered Large Language Models for Heterogeneous Knowledge ReasoningCode1
Context Awareness Gate For Retrieval Augmented GenerationCode1
Multi-modal Retrieval Augmented Multi-modal Generation: A Benchmark, Evaluate Metrics and Strong BaselinesCode1
LaB-RAG: Label Boosted Retrieval Augmented Generation for Radiology Report GenerationCode1
Human-Calibrated Automated Testing and Validation of Generative Language Models—0
RAMIE: Retrieval-Augmented Multi-task Information Extraction with Large Language Models on Dietary Supplements—0
Multi-Reranker: Maximizing performance of retrieval-augmented generation in the FinanceRAG challengeCode2
Document Haystacks: Vision-Language Reasoning Over Piles of 1000+ DocumentsCode0
Traditional Chinese Medicine Case Analysis System for High-Level Semantic Abstraction: Optimized with Prompt and RAG—0
From MTEB to MTOB: Retrieval-Augmented Classification for Descriptive GrammarsCode0
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