  Large language models (LLMs) have show great ability in various natural
language tasks. However, there are concerns that LLMs are possible to be used
improperly or even illegally. To prevent the malicious usage of LLMs, detecting
LLM-generated text becomes crucial in the deployment of LLM applications.
Watermarking is an effective strategy to detect the LLM-generated content by
encoding a pre-defined secret watermark to facilitate the detection process.
However, the majority of existing watermark methods leverage the simple hashes
of precedent tokens to partition vocabulary. Such watermark can be easily
eliminated by paraphrase and correspondingly the detection effectiveness will
be greatly compromised. Thus, to enhance the robustness against paraphrase, we
propose a semantics-based watermark framework SemaMark. It leverages the
semantics as an alternative to simple hashes of tokens since the paraphrase
will likely preserve the semantic meaning of the sentences. Comprehensive
experiments are conducted to demonstrate the effectiveness and robustness of
SemaMark under different paraphrases.
