Large Language Models (LLMs) went from non-existent to ubiquitous in the machine
learning discourse within a few years. Due to the fast pace of the field, it is difficult to identify …

Recent advances in the capabilities of large language models such as GPT-4 have spurred
increasing concern about our ability to detect AI-generated text. Prior works have suggested …

The recent advancements in large language models (LLMs) have sparked a growing
apprehension regarding the potential misuse. One approach to mitigating this risk is to …

With the remarkable generation performance of large language models, ethical and legal
concerns about using them have been raised, such as plagiarism and copyright issues. For …

We construct the first provable watermarking scheme for language models with public
detectability or verifiability: we use a private key for watermarking and a public key for …

Watermarking techniques offer a promising way to secure data via embedding covert
information into the data. A paramount challenge in the domain lies in preserving the …

We introduce a cryptographic method to hide an arbitrary secret payload in the response of
a Large Language Model (LLM). A secret key is required to extract the payload from the …

To mitigate the potential misuse of large language models (LLMs), recent research has
developed watermarking algorithms, which restrict the generation process to leave an …

We introduce a cryptographic method to hide an arbitrary secret payload in the response of
a Large Language Model (LLM). A secret key is required to extract the payload from the …

The advent of Large Language Models (LLMs) has revolutionized text generation, producing
outputs that closely mimic human writing. This blurring of lines between machine-and …