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About this paper
Daniel M. Ziegler, Seraphina Nix, Lawrence Chan, Tim Bauman, Peter Schmidt-Nielsen, Tao Lin, Adam Scherlis, Noa Nabeshima and 4 more
Redwood Research developed and evaluated iterative human-in-the-loop adversarial training, enhanced by novel tool-assisted human attacks, to improve high-stakes reliability for a language model-based classifier. The study demonstrated that extremely low false negative rates can be achieved with minimal impact on output quality in a safe story generation task.
Key insights
- Augmenting human adversaries with AI-powered tools (saliency maps, token suggestions) significantly increases their efficiency in finding subtle or complex adversarial examples.
- Extremely conservative classifier thresholds can achieve very low false negative rates on high-stakes tasks with minimal degradation to the quality of filtered outputs.
- Iterative adversarial training effectively hardens models against human-generated adversarial attacks and improves generalization without compromising average-case performance on typical inputs.
Summary by alphaXiv.
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