Home

Awesome

Retrieval-Augmented Generation for AI-Generated Content: A Survey

This repo is constructed for collecting and categorizing papers about RAG according to our survey paper: Retrieval-Augmented Generation for AI-Generated Content: A Survey. Considering the rapid growth of this field, we will continue to update both paper and this repo.

Overview

<div aligncenter><img width="900" alt="image" src="https://github.com/hymie122/RAG-Survey/blob/main/RAG_Overview.jpg">

Catalogue

Methods Taxonomy

RAG Foundations

<div aligncenter><img width="900" alt="image" src="https://github.com/hymie122/RAG-Survey/blob/main/RAG_Foundations.png">

RAG Enhancements

<div aligncenter><img width="900" alt="image" src="https://github.com/hymie122/RAG-Survey/blob/main/RAG_Enhancements.png">

Applications Taxonomy

<div aligncenter><img width="900" alt="image" src="https://github.com/hymie122/RAG-Survey/blob/main/Applications.png"> <div aligncenter><img width="900" alt="image" src="https://github.com/hymie122/RAG-Survey/blob/main/RAG_Applications.png">

RAG for Text

RAG for Code

RAG for Audio

RAG for Image

RAG for Video

RAG for 3D

RAG for Knowledge

RAG for Science

Benchmark

Benchmarking Large Language Models in Retrieval-Augmented Generation

CRUD-RAG: A Comprehensive Chinese Benchmark for Retrieval-Augmented Generation of Large Language Models

ARES: An Automated Evaluation Framework for Retrieval-AugmentedGeneration Systems

RAGAS: Automated Evaluation of Retrieval Augmented Generation

KILT: a Benchmark for Knowledge Intensive Language Tasks

Citation

if you find this work useful, please cite our paper:

@article{zhao2024retrieval,
  title={Retrieval-Augmented Generation for AI-Generated Content: A Survey},
  author={Zhao, Penghao and Zhang, Hailin and Yu, Qinhan and Wang, Zhengren and Geng, Yunteng and Fu, Fangcheng and Yang, Ling and Zhang, Wentao and Cui, Bin},
  journal={arXiv preprint arXiv:2402.19473},
  year={2024}
}