I listened to a podcast with Scott Aaronson that I'd highly recommend [0]. He's a theoretical computer scientist but he was recruited by OpenAI to work on AI safety. He has a very practical view on the matter and is focusing his efforts on leveraging the probabilistic nature of LLMs to provide a digital undetectable watermark. So it nudges certain words to be paired together slightly more than random and you can mathematically derive with some level of certainty whether an output or even a section of an output was generated by the LLM. It's really clever and apparently he has a working prototype in development.

Some work arounds he hasn't figured out yet is asking for an output in language X and then translating it into language Y. But those may still be eventually figured out.

I think watermarking would be a big step forward to practical AI safety and ideally this method would be adopted by all major LLMs.

That part starts around 1 hour 25 min in.

> Scott Aaronson: Exactly. In fact, we have a pseudorandom function that maps the N-gram to, let’s say, a real number from zero to one. Let’s say we call that real number ri for each possible choice i of the next token. And then let’s say that GPT has told us that the ith token should be chosen with probability pi.

https://axrp.net/episode/2023/04/11/episode-20-reform-ai-ali...