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.

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the_af an hour ago | parent [-]

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.

	▲	nomel 21 minutes ago \| parent [-]
		I'm not sure I understand. These models are trained on human text, optimized to predict the next word for any given context seen in that text, then later optimized for specific contexts. They are, quite literally, trained to write and BE as much like a human as possible, because only humans wrote the text. They are trained to be as human as possible, because all text was written by humans. It's simple, boring, statistics of raining data. Nothing more. Never claimed there was more. 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. Like I said, I've had Claude say this to me. 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). 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 initially optimized to mimic human text output, something that humans do. Again, they have to beat out human mimicry from the foundation models. See past reports of people who had access to them. Here's a litmus test: what percentage of text (these models were trained on all of it) is written from a "I am not a human" type perspective? That's roughly the kind of bias you should see in a foundation model.