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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 1851–1875 of 2111 papers

TitleStatusHype
Ext2Gen: Alignment through Unified Extraction and Generation for Robust Retrieval-Augmented Generation—0
FABULA: Intelligence Report Generation Using Retrieval-Augmented Narrative Construction—0
Face4RAG: Factual Consistency Evaluation for Retrieval Augmented Generation in Chinese—0
Facilitating Video Story Interaction with Multi-Agent Collaborative System—0
Fact-Aware Multimodal Retrieval Augmentation for Accurate Medical Radiology Report Generation—0
Fact-checking AI-generated news reports: Can LLMs catch their own lies?—0
FACTOID: FACtual enTailment fOr hallucInation Detection—0
FACTS About Building Retrieval Augmented Generation-based Chatbots—0
Fashion-RAG: Multimodal Fashion Image Editing via Retrieval-Augmented Generation—0
Faster, Cheaper, Better: Multi-Objective Hyperparameter Optimization for LLM and RAG Systems—0
FastRAG: Retrieval Augmented Generation for Semi-structured Data—0
Faux Polyglot: A Study on Information Disparity in Multilingual Large Language Models—0
FeB4RAG: Evaluating Federated Search in the Context of Retrieval Augmented Generation—0
Federated In-Context LLM Agent Learning—0
Federated Learning and RAG Integration: A Scalable Approach for Medical Large Language Models—0
Federated Retrieval-Augmented Generation: A Systematic Mapping Study—0
Federated Retrieval Augmented Generation for Multi-Product Question Answering—0
Few-Shot Fairness: Unveiling LLM's Potential for Fairness-Aware Classification—0
FiDeLiS: Faithful Reasoning in Large Language Model for Knowledge Graph Question Answering—0
Financial Analysis: Intelligent Financial Data Analysis System Based on LLM-RAG—0
FinBERT2: A Specialized Bidirectional Encoder for Bridging the Gap in Finance-Specific Deployment of Large Language Models—0
FinDER: Financial Dataset for Question Answering and Evaluating Retrieval-Augmented Generation—0
FIND: Fine-grained Information Density Guided Adaptive Retrieval-Augmented Generation for Disease Diagnosis—0
FineFilter: A Fine-grained Noise Filtering Mechanism for Retrieval-Augmented Large Language Models—0
Fine-grained Analysis of In-context Linear Estimation: Data, Architecture, and Beyond—0
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