I'm generally OK with the list of push-backs against common misconceptions in the summary, but I have my doubts about the second one:

Claim: Hallucinations are inevitable. Finding: They are not, because language models can abstain when uncertain.

...which raises the question of how reliable the uncertainty estimate could get (we are not looking for perfection here: humans, to varying degrees, have the same problem.)

For a specific context, consider those cases where LLMs are programming and invent a non-existent function: are they usually less certain about that function than they are about the real functions they use? And even if so, abandoning the task with the equivalent of "I don't know [how to complete this task]" is not very useful, compared to what a competent human programmer would do: check whether such a function exists, and if not, decide whether to implement it themselves, or backtrack to the point where they can solve the problem without it.

More generally, I would guess that balancing the competing incentives to emit a definite statement or decline to do so could be difficult, especially if the balance is sensitive to the context.