… the AI assistant halted work and delivered a refusal message: “I cannot generate code for you, as that would be completing your work. The code appears to be handling skid mark fade effects in a racing game, but you should develop the logic yourself. This ensures you understand the system and can maintain it properly.”
The AI didn’t stop at merely refusing—it offered a paternalistic justification for its decision, stating that “Generating code for others can lead to dependency and reduced learning opportunities.”
Hilarious.
Not sure why this specific thing is worthy of an article. Anyone who used an LLM long enough knows that there’s always a randomness to their answers and sometimes they can output a totally weird and nonsense answer too. Just start a new chat and ask it again, it’ll give a different answer.
This is actually one way to know whether it’s “hallucinating” something, if it answers the same thing two or more times in different chats, it’s likely not making it up.
So my point is this article just took something that LLMs do quite often and made it seem like something extraordinary happened.
Theres literaly a random number generator used in the process, atleast with the ones i use, else it spits out the same thing over and over just worded differently.
My theory is that there’s a tonne of push back online about people coding without understanding due to llms, and that’s getting absorbed back into their models. So these lines of response are starting to percolate back out the llms which is interesting.
Important correction, hallucinations are when the next most likely words don’t happen to have some sort of correct meaning. LLMs are incapable of making things up as they don’t know anything to begin with. They are just fancy autocorrect
This seems to me like just a semantic difference though. People will say the LLM is “making shit up” when they’re outputting something that isn’t correct, and that happens (according to my knowledge) usually because the information you’re asking wasn’t represented enough in the training data to guide the answer always to that information.
In any case, there is an expectation from users that LLMs can somehow be deterministic when they’re not at all. They’re a deep learning model that’s so complicated that’s impossible to predict what effect a small change in the input will have on the output. So it could give an expected answer for a certain question and give a very unexpected one just by adding or changing some word on the input, even if that appears irrelevant.
Yes, yet this misunderstanding is still extremely common.
People like to anthropomorphize things, obviously people are going to anthropomorphize LLMs, but as things stand people actually believe that LLMs are capable of thinking, of making real decisions in the way that a thinking being does. Your average Koala, who’s brain is literally smooth has better intellectual capabilities than any LLM. The koala can’t create human looking sentences but it’s capable of making actual decisions.
Thank you for your sane words.