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In this paper, Kirchenbauer et. al. use a novel watermarking technology to watermark the output of large language models (LLMs) like ChatGP, which is often in the form of AI-generated text, and mitigate the harms associated with the increasing usage of these technologies. They note some of the capabilities of these LLM models as writing documents, creating executable code, and answering questions, often with human-like capabilities. In addition, they list some of the harms as social engineering and election manipulation campaigns that exploit automated bots on social media platforms, creation of fake news and web content, and use of AI systems for cheating onacademic writing and coding assignments. As for implications for policy makers, this technology can be utilized as a means to regulate and oversee the use of these LLMs on all public and social fronts where their AI-generated text output could pose a potential harm, such as those listed by the authors. (Methods and Metrics, watermarking LLM output) 

The tweakable Even-Mansour construction yields a tweakable block cipher from a public random permutation. We prove post-quantum security of tweakable Even-Mansour when attackers have quantum access to the random permutation but only classical access to the secretly-keyed construction, the relevant setting for most real-world applications. We then use our results to prove post-quantum security—in the same model—of the symmetric-key schemes Chaskey (an ISO-standardized MAC), Elephant (an AEAD finalist of NIST’s lightweight cryptography standardization effort), and a variant of Minalpher (an AEAD second-round candidate of the CAESAR competition).