Several commenters who have not read the abstract of the paper are mentioning LLM-detection tools. That is not what is being shown here. Rather they are saying

Several commenters who have not read the abstract of the paper are mentioning LLM-detection tools. That is not what is being shown here. Rather they are saying how to modify the design of an LLM to deliberately inject watermarks into generated text such that it will be possible to detect that the text came from a particular LLM. While interesting in the abstract, I think I can definitively say that absolutely nobody wants this. People trying to pass off LLM content (whether students or content providers) as human-written are not interested in being detected. People who are using LLMs to get information for their own knowledge or amusement or as a cybernetic augmentation do not need this. LLM providers want to drive adoption, and if you can be exposed as passing off LLM slop as your own, then nobody will use their stuff.