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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 2076–2100 of 2111 papers

TitleStatusHype
Deep Equilibrium Object DetectionCode1
Retrieval Augmented Generation and Representative Vector Summarization for large unstructured textual data in Medical EducationCode1
Retrieval Augmented Generation using Engineering Design KnowledgeCode0
Prompt Generate Train (PGT): Few-shot Domain Adaption of Retrieval Augmented Generation Models for Open Book Question-Answering—0
Merging-Diverging Hybrid Transformer Networks for Survival Prediction in Head and Neck CancerCode1
TRAQ: Trustworthy Retrieval Augmented Question Answering via Conformal PredictionCode0
UniPoll: A Unified Social Media Poll Generation Framework via Multi-Objective OptimizationCode0
DeepMerge: Deep-Learning-Based Region-Merging for Image SegmentationCode0
Bridging the Language Gap: Dynamic Learning Strategies for Improving Multilingual Performance in LLMs—0
Retrieval Augmented Chest X-Ray Report Generation using OpenAI GPT models—0
Huatuo-26M, a Large-scale Chinese Medical QA DatasetCode2
Spatio-Temporal driven Attention Graph Neural Network with Block Adjacency matrix (STAG-NN-BA)—0
Robust affine point matching via quadratic assignment on GrassmanniansCode0
Multi-class Brain Tumor Segmentation using Graph Attention Network—0
MuRAG: Multimodal Retrieval-Augmented Generator for Open Question Answering over Images and Text—0
Improving the Domain Adaptation of Retrieval Augmented Generation (RAG) Models for Open Domain Question Answering—0
Robust Action Governor for Uncertain Piecewise Affine Systems with Non-convex Constraints and Safe Reinforcement Learning—0
Re2G: Retrieve, Rerank, GenerateCode1
Retrieval-augmented Generation across Heterogeneous Knowledge—0
R3 : Refined Retriever-Reader pipeline for Multidoc2dial—0
End-to-End Table Question Answering via Retrieval-Augmented Generation—0
Re2G: Retrieve, Rerank, Generate—0
Continual Stereo Matching of Continuous Driving Scenes With Growing ArchitectureCode0
Superpixel-Based Building Damage Detection from Post-earthquake Imagery Using Deep Neural Networks—0
Variational Learning for Unsupervised Knowledge Grounded DialogsCode0
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