Web: http://arxiv.org/abs/2205.01663

Sept. 16, 2022, 1:16 a.m. | Daniel M. Ziegler, Seraphina Nix, Lawrence Chan, Tim Bauman, Peter Schmidt-Nielsen, Tao Lin, Adam Scherlis, Noa Nabeshima, Ben Weinstein-Raun, Daniel

cs.CL updates on arXiv.org arxiv.org

In the future, powerful AI systems may be deployed in high-stakes settings,
where a single failure could be catastrophic. One technique for improving AI
safety in high-stakes settings is adversarial training, which uses an adversary
to generate examples to train on in order to achieve better worst-case
performance.


In this work, we used a language generation task as a testbed for achieving
high reliability through adversarial training. We created a series of
adversarial training techniques -- including a tool that …

arxiv reliability training

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