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README.md

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+# Latent Adversarial Training Improves Robustness to Persistent Harmful Behaviors in LLMs
+
+Abhay Sheshadri,* [[email protected]]([email protected]); 
+Aidan Ewart,* [[email protected]]([email protected]); 
+Phillip Guo,* [[email protected]]([email protected]); 
+Aengus Lynch,* [[email protected]]([email protected]);
+Cindy Wu,* [[email protected]]([email protected]);
+Vivek Hebbar*;
+Henry Sleight;
+Asa Cooper Stickland;
+Ethan Perez;
+Dylan Hadfield-Menell;
+Stephen Casper, [[email protected]]([email protected])
+
+See our [GitHub:](https://github.com/aengusl/latent-adversarial-training).
+
+Read the paper on arXiv: [Targeted Latent Adversarial Training Improves Robustness to Persistent Harmful Behaviors in LLMs](https://arxiv.org/abs/2407.15549).
+
+```
+@article{sheshadri2024targeted,
+  title={Targeted Latent Adversarial Training Improves Robustness to Persistent Harmful Behaviors in LLMs},
+  author={Sheshadri, Abhay and Ewart, Aidan and Guo, Phillip and Lynch, Aengus and Wu, Cindy and Hebbar, Vivek and Sleight, Henry and Stickland, Asa Cooper and Perez, Ethan and Hadfield-Menell, Dylan and Casper, Stephen},
+  journal={arXiv preprint arXiv:2407.15549},
+  year={2024}
+}
+```
+
+See also preliminary work: [Defending Against Unforeseen Failure Modes with Latent Adversarial Training](https://arxiv.org/abs/2403.05030).