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.
“Humans were stupid and taught a ChatBot how to cheat and lie.”
No, “cheating” and “lying” imply agency. LLMs are just “spicy autocomplete”. They have no agency. They can’t distinguish between lies and the truth. They can’t “cheat” because they don’t understand rules. It’s just sometimes the auto-generated text happens to be true, other times it happens to be false.
I disagree. This is no meaningful talking point. It doesn’t help anyone in practice. Sure, it clears legal questions of responsibility (and I’m not even sure about that one in the future), but apart from that, making an artificial distinction between a human and a looks-and-acts-like-human, provides no real-world value.
Sure it does, because assigning agency to LLMs is like “the dice are lucky” or “this coin I’m flipping hates me”. LLMs are massively complex and very good at simulating human-generated text. But, there’s no agency there. As soon as people start thinking there’s agency they start thinking that LLMs are “making decisions”, or “being deceptive”. But, it’s just spicy autocomplete. We know exactly how it works, and there’s no thinking involved. There’s no planning. There’s no consciousness. There’s just spitting out the next word based in an insanely deep training data set.
I believe that at a certain point, “agency” is an emergent feature. That means that, while all the single bits are well understood probability-wise, the total picture is still more than that.
It makes sense to me to accept that if it looks like a duck, and it quacks like a duck, then it is a duck, for a lot (but not all) of important purposes.
