Text watermarking algorithms are crucial for protecting the copyright of textual content.
Historically, their capabilities and application scenarios were limited. However, recent …

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 …

We present REMARK-LLM, a novel efficient, and robust watermarking framework designed
for texts generated by large language models (LLMs). Synthesizing human-like content …

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 …

This study aims to proactively tackle misuse of large language models beyond identification
of machine-generated text. While existing methods focus on detection, some malicious …

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 …

As the use of Large Language Models (LLMs) in text generation tasks proliferates, concerns
arise over their potential to compromise academic integrity. The education sector currently …

Large language models (LLMs) have opened up enormous opportunities while
simultaneously posing ethical dilemmas. One of the major concerns is their ability to create …

We show the viability of tackling misuses of large language models beyond the identification
of machine-generated text. While existing zero-bit watermark methods focus on detection …