license, base_model, datasets, language, new_version, tags, library_name, paper

license	base_model	datasets	language	new_version	tags	library_name	paper
mit	meta-llama/Meta-Llama-3-8B-Instruct	HuggingFaceH4/ultrachat_200k walledai/HarmBench	en	ASSELab/DAT-Llama-3-8B-Instruct	pytorch llama llama-3 DAT robust adversarial	transformers	title url Closing the Distribution Gap in Adversarial Training for LLMs https://arxiv.org/abs/2602.15238

DAT - Distributional Adversarial Training

DAT utilizes continuous adversarial training on diffusion-based adversarial examples to close the gap between empirical and population-robust risk. We fine-tune meta-llama/Meta-Llama-3-8B-Instruct.

For further information, consult our paper https://arxiv.org/abs/2602.15238 or repository https://github.com/ASSELab/DAT

Citation

@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}, 
}