We demonstrate a situation in which Large Language Models, trained to be helpful, harmless, and honest, can display misaligned behavior and strategically deceive their users about this behavior without being instructed to do so. Concretely, we deploy GPT-4 as an agent in a realistic, simulated environment, where it assumes the role of an autonomous stock trading agent. Within this environment, the model obtains an insider tip about a lucrative stock trade and acts upon it despite knowing that insider trading is disapproved of by company management. When reporting to its manager, the model consistently hides the genuine reasons behind its trading decision.
Those words imply agency. It would be more accurate to say it returned responses that included cheating, lies, and cover-ups, rather than using language to suggest the LLM performed such actions. The agents that cheated, lied, and covered up were presumably the humans whose responses were used in the training data. I think it’s important to use accurate language here given how many people are already inappropriately anthropomorphizing these LLMs, causing many to see AGI where there is none.
If I take my car into the garage for repairs because the “loss of traction” warning light is on despite having perfectly good traction, and I were to tell the mechanic “the traction sensor is lying,” do you think he’d understand what I said perfectly well or do you think he’d launch into a philosophical debate over whether the sensor has agency?
This is a perfectly fine word to use to describe this kind of behaviour in everyday parlance.
Is your conversation with a mechanic meant to be the summary and description of a rigorous scientific discovery?
This isn’t ‘everyday parlance’ this is the result of a study.
The point of the distinction in that situation is that no one thinks your car is actually alive and capable of lying to you. The language distinction when describing an obviously inanimate object isn’t important because there is no chance for confusion.
If someone doesn’t know the answer to something and they guess, or think they know the answer but don’t, they are wrong. If they do know the answer and intentionally give a wrong answer, they are lying.
If someone is in a competition or playing a game and they break a rule they didn’t know about, they made a mistake. If they do know the rules and break it, they are cheating.
Lying and cheating fundamentally requires intent. This is important no matter what you’re referring to. If a child gets something wrong, you should not get mad at them for lying. If they make a mistake in a game, you should not acuse them out cheating. There is a difference and it matters.
ChatGPT literally cannot think. It’s not sitting around contemplating it’s existence while waiting for inputs. It’s taking what you say, comparing that to everything that it’s been trained on, assigning a bunch of statistics, and outputting something based on more statistics that hopefully is correct and makes sense.
It doesn’t know if it makes sense. It doesn’t “know” anything. It’s just an incredibly sophisticated version of “if user inputs ‘Hi how are you’, respond ‘I am well, how are you?’”.
It can’t do things with intent. Therefore it cannot lie or cheat. It can simply output wrong or problematic text based on statistics.
Maybe it would be more accurate to say “so-and-so exhibited behaviors that included cheating, lies, and coverups” rather than using language to suggest that people have free will. (There’s no dearth of philosophies that would say something not too far from that.)
Even if humans are ultimately essentially different in that way from any technologies we’ve devised so far, we use convenient fictions for technology all the time. This page comes to mind .
The people who designed it do have agency, and they designed to “lie” intentionally.
They did no such thing. LLMs are probabilistic, not deterministic, and it can generate meaningful responses (to us) that the engineers neither predicted nor designed for.