Most LLMs seed their output so they can recognize whether something was created by them. I can see how there will be common standards for this and every LLM as it’s in the best interest of every commercial LLM to know whether something is LLM output or not.
Nah that means you can ask an LLM “is this real” and get a correct answer.
That defeats the point of a bunch of kinds of material.
Deepfakes, for instance. International espionage, propaganda, companies who want “real people”.
A simple is_ai checkbox of any kind is undesirable, but those sources will end back up in every LLM, even one that was behaving and flagging its output.
You’d need every LLM to do this, and there’s open source models, there’s foreign ones. And as has already been proven, you can’t rely on an LLM detecting a generated product without it.
The correct way to do it would be to instead organize a not-ai certification for real content. But that would severely limit training data. It could happen once quantity of data isn’t the be-all end-all for a model, but I dunno when when or if that’ll be the case.
Most LLMs seed their output so they can recognize whether something was created by them. I can see how there will be common standards for this and every LLM as it’s in the best interest of every commercial LLM to know whether something is LLM output or not.
Nah that means you can ask an LLM “is this real” and get a correct answer.
That defeats the point of a bunch of kinds of material.
Deepfakes, for instance. International espionage, propaganda, companies who want “real people”.
A simple is_ai checkbox of any kind is undesirable, but those sources will end back up in every LLM, even one that was behaving and flagging its output.
You’d need every LLM to do this, and there’s open source models, there’s foreign ones. And as has already been proven, you can’t rely on an LLM detecting a generated product without it.
The correct way to do it would be to instead organize a not-ai certification for real content. But that would severely limit training data. It could happen once quantity of data isn’t the be-all end-all for a model, but I dunno when when or if that’ll be the case.
LLM watermarking is economically desireble. Why would it be more profitable to train worse LLMs on LLM outputs? I’m curious for any argument.
Also, what has deep-fakes anything to do with LLMs? This is not related at all.
A certificate for “real” content is not feasible. It’s much easier to just prevent LLMs to train on LLM output.