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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Collecting awesome papers of RAG for AIGC. We propose a taxonomy of RAG foundations, enhancements, and applications in paper "Retrieval-Augmented Generation for AI-Generated Content: A Survey".
| Date | Stars |
|---|---|
| 2026-07-24 | 1787 |
| 2026-07-25 | 1787 |
| 2026-07-28 | 1787 |
| 2026-07-30 | 1787 |
| 2026-07-31 | 1788 |
| 2026-08-06 | 1788 |
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growth rate 0.06%/day
# 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*](https://arxiv.org/abs/2402.19473). Considering the rapid growth of this field, we will continue to update both [paper](https://arxiv.org/abs/2402.19473) 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">
- Query-based RAG
[REALM: Retrieval-Augmented Language Model Pre-Training](https://arxiv.org/abs/2002.08909)
[Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection](https://arxiv.org/abs/2310.11511)
[REPLUG: Retrieval-Augmented Black-Box Language Models](https://arxiv.org/abs/2301.12652)
[In-Context Retrieval-Augmented Language Models](https://arxiv.org/abs/2302.00083)
[When Language Model Meets Private Library](https://arxiv.org/abs/2210.17236)
[DocPrompting: Generating Code by Retrieving the Docs](https://openreview.net/pdf?id=ZTCxT2t2Ru)
[Retrieval-based prompt selection for code-related few-shot learning](https://doi.org/10.1109/ICSE48619.2023.00205)
[Inferfix: End-to-end program repair with llms](https://doi.org/10.1145/3611643.3613892)
[Make-an-audio: Text-to-audio generation with prompt-enhanced diffusion models](https://proceedings.mlr.press/v202/huang23i.html)
[Reacc: A retrieval-augmented code completion framework](https://doi.org/10.18653/v1/2022.acl-long.431)
[Uni-parser: Unified semantic parser for question answering on knowledge base and database](https://doi.org/10.18653/v1/2022.emnlp-main.605)
[RNG-KBQA: generation augmented iterative ranking for knowledge base question answering](https://doi.org/10.18653/v1/2022.acl-long.417)
[End-to-end casebased reasoning for commonsense knowledge base completion](https://doi.org/10.18653/v1/2023.eacl-main.255)
[Combining transfer learning with in-context learning using blackbox llms for zero-shot knowledge base question answering](https://doi.org/10.48550/arXiv.2311.08894)
[Genegpt: Augmenting large language models with domain tools for improved access to biomedical information](https://arxiv.org/abs/2304.09667)
[Retrieval-augmented large language models for adolescent idiopathic scoliosis patients in shared decision-making](https://dl.acm.org/doi/10.1145/3584371.3612956)
[Retrievegan:Image synthesis via differentiable patch retrieval](https://link.springer.com/chapter/10.1007/978-3-030-58598-3_15)
[Instance-conditioned gan](https://proceedings.neurips.cc/paper/2021/file/e7ac288b0f2d41445904d071ba37aaff-Paper.pdf)
[Retrieval-Augmented Score Distillation for Text-to-3D Generation](https://doi.org/10.48550/arXiv.2402.02972)
- Latent Representation-based RAG
[Leveraging passage retrieval with generative models for open domain question answering](https://doi.org/10.18653/v1/2021.eacl-main.74)
[Bashexplainer: Retrieval-augmented bash code comment generation based on finetuned codebert](https://doi.org/10.1109/ICSME55016.2022.00016)
[EditSum: A Retrieve-and-Edit Framework for Source Code Summarization](https://doi.org/10.1109/ASE51524.2021.9678724)
[Retrieve and Refine: Exemplar-based Neural Comment Generation](https://arxiv.org/abs/2010.04459)
[RACE: retrieval-augmented commit message generation](https://doi.org/10.18653/v1/2022.emnlp-main.372)
[Unik-qa: Unified representations of structured and unstructured knowledge for open-domain question answering](https://doi.org/10.18653/v1/2022.findings-naacl.115)
[A Retrieve-and-Edit Framework for Predicting Structured OExcerpt of 49,932 characters
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Ikko Eltociear Ashimine · Japan
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Tianshu Wang
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:e859ecd0d29be335, topic:rag, desc:retrieval-augmented generation, readme:retrieval-augmented generation
matched fp:e859ecd0d29be335, topic:llm
matched fp:e859ecd0d29be335, topic:diffusion-models