May 7, 2024, 4:45 a.m. | Nicholas Carlini, Milad Nasr, Christopher A. Choquette-Choo, Matthew Jagielski, Irena Gao, Anas Awadalla, Pang Wei Koh, Daphne Ippolito, Katherine Lee

cs.LG updates on arXiv.org arxiv.org

arXiv:2306.15447v2 Announce Type: replace-cross
Abstract: Large language models are now tuned to align with the goals of their creators, namely to be "helpful and harmless." These models should respond helpfully to user questions, but refuse to answer requests that could cause harm. However, adversarial users can construct inputs which circumvent attempts at alignment. In this work, we study adversarial alignment, and ask to what extent these models remain aligned when interacting with an adversarial user who constructs worst-case inputs (adversarial …

abstract adversarial alignment arxiv construct creators cs.ai cs.cl cs.cr cs.lg harm however inputs language language models large language large language models networks neural networks questions type

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