Persona vectors: Monitoring and controlling character traits in language models

Unfortunately, this research seems to use a very coarse method (giving the model instructions to be evil and then measuring its activation changes against a “non evil” model). However, this is not a self supervised approach — it requires you input your own heavy handed concept of persona into the system. Obviously a more complex and complete personality is more than the sum of your yes/no answers to personality test questions.

However, it’s very possible with low rank methods to soon perhaps be able to give models long lived, user-specific personalities that emerge across thousands of conversations. That’s what I would happily call a persona vector.

That is almost exactly what they are and what you should treat them as.

A lossy compressed corpus of publicly available information with a weight of randomness. The most fervent skeptics like to call LLMs "autocorrect on steroids" and they are not really wrong.

Here's the thing, not every question has an objectively correct answer. I'd say almost no question does. Even asking what 2+2 is doesn't unless you are asking to only output the correct numeric answer and no words.

Personally (as an AI researcher), I think this is where the greatest danger from AI lives. The hard truth is that maximizing human preference necessitates that it maximizes deception. Correct answers are not everybody's preference. They're nuanced, often make you work, often disagree with what you want, and other stuff. I mean just look at Reddit. The top answer is almost never the correct answer. It frequently isn't even an answer! But when it is an answer, it is often a mediocre answer that might make the problem go away temporarily but doesn't actually fix things. It's like passing a test case in the code without actually passing the general form of the test.

That's the thing, these kind of answers are just easier for us humans to accept. Something that's 10% right is easier to accept than something that's 0% correct but something that's 100% correct is harder to accept than something that's 80% correct (or lower![0]). So people prefer a little lie. Which of course this is true! When you teach kids physics you don't teach them everything at once! You teach them things like E=mc2 and drop the momentum part. You treat everything as a spherical chicken in a vacuum. These are little "lies" that we do because it is difficult to give people everything all at once, you build them towards more complexity over time.

Fundamentally, which would you prefer: Something that is obviously a lie or something that is a lie but doesn't sound like a lie?

Obviously the answer is the latter case. But that makes these very difficult tools to use. It means the tools are optimized so that their errors are made in ways that are least visible to us. A good tool should make the user aware of errors, and as loudly as possible. That's the danger of these systems. You can never trust them[1]

[0] I say that because there's infinite depth to even the most mundane of topics. Try working things out from first principles with no jump in logic. Connect every dot. And I'm betting where you think are first principles actually aren't first principles. Even just finding what those are is a very tricky task. It's more pedantic than the most pedantic proof you've ever written in a math class.

[1] Everyone loves to compare to humans. Let's not anthropomorphize too much. Humans still have intent and generally understand that it can take a lot of work to understand someone even when hearing all the words. Generally people are aligned, making that interpretation easier. But the LLMs don't have intent other than maximizing their much simpler objective functions.