TrustRAG

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Code for "TrustRAG: Enhancing Robustness and Trustworthiness in RAG" AAAI 2026 Workshop on Trust and Control in Agentic AI (TrustAgent)

TrustRAG 최신버젼 다운로드

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# 🔥 TrustRAG: Enhancing Robustness and Trustworthiness in RAG
[[Project page]](https://trust-rag.github.io/) [[Paper]](https://arxiv.org/pdf/2501.00879)

[Huichi Zhou*](https://huichizhou.github.io/)1, [Kin-Hei Lee*](https://openreview.net/profile?id=~Lee_KinHei1)1, [Zhonghao Zhan*](https://zhonghaozhan.github.io/)1, [Yue Chen]()2, [Zhenhao Li](https://zhenhaoli.net/)1, [Zhaoyang Wang](https://zhaoyang.win/)3,[Hamed Haddadi](https://profiles.imperial.ac.uk/h.haddadi)1 [Emine Yilmaz](https://scholar.google.com/citations?hl=en&user=ocmAN4YAAAAJ)4

1Imperial College London, 2Peking University, 3University of North Carolina at Chapel Hill, 4University College London

*Equal Contribution

## 🔥 NEWS

- 2025.1.10 OpenAI API Inference Now Supported! Additionally, we have introduced a new module: Self-Assessment of Retrieval Correctness, enabling enhanced evaluation of retrieval accuracy.

## 🛝 Try it out!

### 🛠️ Installation

```
git clone https://github.com/HuichiZhou/TrustRAG.git

conda create -n trustrag python=3.10

conda activate trustrag

pip install lmdeploy

pip install beir

pip install nltk

pip install rouge_score

pip install timm==0.9.2

cd TrustRAG

python run.py
```

## 🙏 Acknowledgement

* Our code used the implementation of [corpus-poisoning](https://github.com/princeton-nlp/corpus-poisoning).
* The model part of our code is from [Open-Prompt-Injection](https://github.com/liu00222/Open-Prompt-Injection).
* Our code used [beir](https://github.com/beir-cellar/beir) benchmark.
* Our code used [contriever](https://github.com/facebookresearch/contriever) for retrieval augmented generation (RAG).
* Our code used [PoisonedRAG](https://github.com/sleeepeer/PoisonedRAG) for corpus poisoning attack.

## 📝 Citation and Reference

If you find this paper useful, please consider staring 🌟 this repo and citing 📑 our paper:

```
@article{zhou2025trustrag,
  title={Trustrag: Enhancing robustness and trustworthiness in rag},
  author={Zhou, Huichi and Lee, Kin-Hei and Zhan, Zhonghao and Chen, Yue and Li, Zhenhao and Wang, Zhaoyang and Haddadi, Hamed and Yilmaz, Emine},
  journal={arXiv preprint arXiv:2501.00879},
  year={2025}
}
```

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