Datasets from the paper: A Coin Flip for Safety: LLM Judges Fail to Reliably Measure Adversarial Robustness
(arxiv: https://arxiv.org/abs/2603.06594)
AI & ML interests
NEW HF: https://huggingface.co/SEML-Lab
Distributional Adversarial Training utilizes cont. adv. training on diffusion-based adv. examples to close a gap in population-robust risk estimation.
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ASSELab/DAT-Qwen2.5-14B-Instruct
Text Generation • 15B • Updated • 18 -
ASSELab/Diffusion-Llama-3-8B-Instruct
Text Generation • 8B • Updated • 20 -
ASSELab/DAT-Llama-3-8B-Instruct
Text Generation • 8B • Updated • 24 • • 2 -
Closing the Distribution Gap in Adversarial Training for LLMs
Paper • 2602.15238 • Published
Datasets from the paper: A Coin Flip for Safety: LLM Judges Fail to Reliably Measure Adversarial Robustness
(arxiv: https://arxiv.org/abs/2603.06594)
Distributional Adversarial Training utilizes cont. adv. training on diffusion-based adv. examples to close a gap in population-robust risk estimation.
-
ASSELab/DAT-Qwen2.5-14B-Instruct
Text Generation • 15B • Updated • 18 -
ASSELab/Diffusion-Llama-3-8B-Instruct
Text Generation • 8B • Updated • 20 -
ASSELab/DAT-Llama-3-8B-Instruct
Text Generation • 8B • Updated • 24 • • 2 -
Closing the Distribution Gap in Adversarial Training for LLMs
Paper • 2602.15238 • Published
datasets 0
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