Comment by nomel
5 hours ago
Nobody is concluding that. These models are trained on human text. It's just statistics. It will respond like a human because it was trained on human text. They have to beat the hell out of the foundation models to get push the statistics how they are. I don't see this as anything but boring residuals of not beating hard enough.
Yes, you are concluding this in the initial comment of this chain.
LLMs cannot get "tired" or "lazy", that's just you projecting animal behavior on something that's not an animal.
Now you're moving the goal posts, "it resembles a human". Well, you're primed to consider it one. ELIZA also "resembled" a human in that sense, but I don't think you would claim it could get bored or lazy. Nor that you could extrapolate to it from human behavior.
In any case, if you've seen online discourse, people rarely admit they are tired.
I'm not sure I understand.
I'm not moving a goal post. You're just thinking I'm making a point that I'm not. As I've said several times, it's just boring statistics. Those statistics are optimized to mimic human output. They are, quite literally, trained to write and BE as much like a human as possible, because only humans wrote the text, and they're optimized to predict the next word a human would write. Alignment is partly about removing the models perception of human self. See reports of people who had access to them, pre alignment. This is statistically sound.
It's statistics optimized to predict the next word a human would write, to mimic a human writing as closely as possible, because that is the loss function. Don't assume I think there's more to it.
This does not mean they contain systems that let them get tired. But, this does mean there are latent spaces that progress to generating text that contain text driven by human biology, because it's in the training data. I've also had Claude refer to itself as "she". Does that mean it's a woman? No, it means there was a little bit extra "she" mentions in the training data (btw, this 100% repeatable behavior left with 3.7. They probably cleaned the data a bit better, or hammered it out in alignment).
What percentage of text (these models were trained on all of it) is written from a "I am not a human" type perspective vs from a "I am human" perspective? That's roughly the kind of bias you should see in a base model.
edit: rearranged and reduced redundancy.
Ok, I indeed misunderstood your point.
I'm not sold on the idea that as the chat session goes longer, the probability of an LLM saying "I'm tired" is increased; I'm not convinced this is modeled in LLMs at all. As for what you call "laziness" manifesting in a longer session, I think that's more likely due to context rot than to any kind of statistical modeling of human laziness.
But yes, now I see your point was different to what I thought you were saying. Apologies!
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