[ComputerGuru]

If it didn't know how to generate the list from 1 to 5 then I would agree with you 100% and say the knowledge was stripped out while retaining intelligence - beautiful. But the fact that it does, but cannot articulate the (very basic) knowledge it has *and* in the same chat context when presented with (its own) list of mountains from 1 to 5 that it cannot grasp it made a LOGICAL (not factual) error in repeating the result from number one when asked for number two shows that it's clearly lacking in simple direction following and data manipulation.

[LeifCarrotson]

> the knowledge was stripped out while retaining intelligence ... it cannot grasp it made a LOGICAL (not factual) error...

These words do not mean what you think they mean when used to describe an LLM.

[parineum]

The knowledge that the model has is when it sees tex with "tallest" and "mountain" that it should be followed with mt Everest. Unless it also has "list", in which case, it makes a list.

[gf000]

Have you used an LLM? I mean the actual large models? Because they do the exact same errors, just on a slightly less frequent/better hidden manner.

[ComputerGuru]

Yes, and obviously this is a question of metrics/spectrum. But this is pretty bad, even compared to several generations old tech (at admittedly much larger size).

[ezst]

Why would there be logic involved? This is a LLM, not electronic intelligence.