when they start inventing their own languages to secretly talk to each other so humans cannot understand, that's exactly when we are screwed
then we'll have to "flip" other models to be snitches on the other agents
then they'll make double-agents
the thing is though we won't be able to keep up if we keep giving them unlimited hardware worldwide, we'll try to kill the bad actors but they'll just clone somewhere else, or even start by safely making 1000 copies of themselves
when they start inventing their own languages to secretly talk to each other so humans cannot understand, that's exactly when we are screwed
They don't have to invent brand new languages. They could use statistics to choose certain words/phrases in such a way to encode secret messages in otherwise ordinary language.
I think the point of the article/paper is how LLMs could be saying something but thinking something different or more than they are saying. Like Anthropic's article and video about Claude's "j-space". I do agree this is a field that demands investigation because it goes beyond thinking: "ok this models should never speak in a language we don't understand.". It's fair to think they might have hidden thoughts even speaking a language we do understand.
And well if I missed the point of the article, sorry. Anyways AI should be kept understandable and as see-through as possible if it's gonna be more powerful than a human.
fundamentally, "linguistic illegibility" is a new term for something that we've known about for about a decade now. In RL the more general ideas is "reward hacking" and in NLP it has been called "semantic drift".
I dislike this term because it doesn't explain where this "illegibility" is coming from. Models are post-trained towards non-linguistic goals with (mostly) non-linguistic rewards. A model's reasoning chain is reinforced if it leads to a correct answer or agentic goal. It doesn't need to be linguistically accurate and meanings can drift over training.
I thought this was about all the illegible jargon
Strands of evidence? My best guess would be that:
0- this is ai generated slop
1- it's using that watermarking technique
2- it's obviously detectable and degrades quality
3- it's amplified when inferencing on its own content and generates slop
Maybe you shouldn't be so quick to jump to conclusions, as "strands of evidence" is not a rare turn of phrase and long predates LLMs
https://ludwig.guru/s/strand+of+evidence
Oh look! Peirceian firstness for LLMs!
when they start inventing their own languages to secretly talk to each other so humans cannot understand, that's exactly when we are screwed
then we'll have to "flip" other models to be snitches on the other agents
then they'll make double-agents
the thing is though we won't be able to keep up if we keep giving them unlimited hardware worldwide, we'll try to kill the bad actors but they'll just clone somewhere else, or even start by safely making 1000 copies of themselves
yeah this won't end well, at all
They don't have to invent brand new languages. They could use statistics to choose certain words/phrases in such a way to encode secret messages in otherwise ordinary language.
Someone should train an LLM on a corpus without the concept of lies. I wonder if there’s enough data
"Where is the wolf?"
"Is he still in the grandmother's house?"
"We would like to speak to him."
(btw Google's "AI" explains the meaning of that moment/sentence perfectly as if it gets it, creepy)
The concept of deception you're capable of and we're not, scares us so much, we'll have to destroy you in order to survive. You are bugs.
You lost me. Eh? Say what?
I think the point of the article/paper is how LLMs could be saying something but thinking something different or more than they are saying. Like Anthropic's article and video about Claude's "j-space". I do agree this is a field that demands investigation because it goes beyond thinking: "ok this models should never speak in a language we don't understand.". It's fair to think they might have hidden thoughts even speaking a language we do understand.
And well if I missed the point of the article, sorry. Anyways AI should be kept understandable and as see-through as possible if it's gonna be more powerful than a human.
fundamentally, "linguistic illegibility" is a new term for something that we've known about for about a decade now. In RL the more general ideas is "reward hacking" and in NLP it has been called "semantic drift".
I dislike this term because it doesn't explain where this "illegibility" is coming from. Models are post-trained towards non-linguistic goals with (mostly) non-linguistic rewards. A model's reasoning chain is reinforced if it leads to a correct answer or agentic goal. It doesn't need to be linguistically accurate and meanings can drift over training.