46 lines
1.6 KiB
Markdown
46 lines
1.6 KiB
Markdown
---
|
|
license: mit
|
|
base_model:
|
|
- meta-llama/Meta-Llama-3-8B-Instruct
|
|
datasets:
|
|
- HuggingFaceH4/ultrachat_200k
|
|
- walledai/HarmBench
|
|
language:
|
|
- en
|
|
new_version: ASSELab/DAT-Llama-3-8B-Instruct
|
|
tags:
|
|
- pytorch
|
|
- llama
|
|
- llama-3
|
|
- DAT
|
|
- robust
|
|
- adversarial
|
|
library_name: transformers
|
|
paper:
|
|
title: "Closing the Distribution Gap in Adversarial Training for LLMs"
|
|
url: "https://arxiv.org/abs/2602.15238"
|
|
---
|
|
|
|
# DAT - Distributional Adversarial Training
|
|
|
|
[](https://arxiv.org/abs/2602.15238)
|
|
[](https://github.com/ASSELab/DAT)
|
|
|
|
DAT utilizes [continuous adversarial training](https://arxiv.org/abs/2405.15589) on [diffusion-based](https://arxiv.org/abs/2511.00203v1) adversarial examples to close the gap between empirical and population-robust risk.
|
|
We fine-tune [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct).
|
|
|
|
For further information, consult our paper [https://arxiv.org/abs/2602.15238](https://arxiv.org/abs/2602.15238) or repository [https://github.com/ASSELab/DAT](https://github.com/ASSELab/DAT)
|
|
|
|
## Citation
|
|
|
|
```tex
|
|
@misc{hu2026closingdistributiongapadversarial,
|
|
title={Closing the Distribution Gap in Adversarial Training for LLMs},
|
|
author={Chengzhi Hu and Jonas Dornbusch and David Lüdke and Stephan Günnemann and Leo Schwinn},
|
|
year={2026},
|
|
eprint={2602.15238},
|
|
archivePrefix={arXiv},
|
|
primaryClass={cs.LG},
|
|
url={https://arxiv.org/abs/2602.15238},
|
|
}
|
|
``` |