So I understand the author has high standing in the community.

But I think they are making actually disingenuous arguments by mixing assertions that are true but irrelevant together with assertions that are probably wrong.

For example we can break down the following firehose of assertions by the author:

Performance is amazing ... but ... they make stupid mistakes
Factual errors, logical errors, inconsistency, limited reasoning, toxicity...
LLMs have no knowledge of the underlying reality
They have no common sense & they can’t plan their answer
Unpopular Opinion about AR-LLMs
Auto-Regressive LLMs are doomed.
They cannot be made factual, non-toxic, etc.
They are not controllable

> they make stupid mistakes

OK maybe some make stupid mistakes, but it's clear that increasingly advanced GPT-N are making fewer of them.

> Factual errors

Raw LLMs are pure bullshitters but it turns out that facts usually make better bullshit (in its technical sense) than lies. So advanced GPT-N usually are more factual. Furthermore, raw GPT-4 (before reinforcement training) has excellent calibration of its certainty of its beliefs as shown in Figure 8 of the technical report, at least for multiple choice questions.

> logical errors

Same thing. More advanced ones make fewer logical errors, for whatever reason. It's an emergent property.

> inconsistency

Nothing about LLMs requires consistency just like nothing about human meaning and understanding requires consistency, but more advanced LLMs emergently give more coherent continuations. This is especially funny because the opposite argument used to be given for why robots will never be on the level of humans - robots are C3P0-like mega-dorks whose wiring will catch fire and circuit boards will explode if we ask them to follow two conflicting rules.

> limited reasoning

Of course their reasoning is limited. Our reasoning is limited too. Larger language models appear to have less-limited reasoning.

> toxicity

There is nothing saying that raw LLMs won't be toxic. Probably they will be, according to most definitions. That's why corporations lobomize them with human feedback reinforcement learning as a final 'polishing' step. Some humans are huge assholes too, but probably they have meaning and understanding anyway.

> LLMs have no knowledge of the underlying reality

OK fine you can say that any p-zombie has no knowledge of the underlying reality if you want, if that's your objection. Or maybe they are saying LLMs don't have pixel buffer visual or time series audio inputs. Does that mean when those are added (they have already been added) then LLMs can possibly get meaning and understanding?

> They have no common sense

If you say that inhuman automata are by definition incapable of common sense then sure they have no common sense. But if you are talking about testing for common sense, then GPT-N is unlocking a mindblowing amount of common sense as N is increasing.

> they can’t plan their answer

Probably they are saying this because of next-token-prediction which is tautologically true, in the same way that it's true that humans speak one word after another. But the implication is wrong. They can plan their answer in any sense that matters.

> Auto-Regressive LLMs are doomed.

OK. Do you mean in terms of technical capabilities, or in terms of societal acceptance? They are different things. Or do you mean they are doomed to never attain meaning and understanding?

> They cannot be made factual, non-toxic, etc. They are not controllable.

Those same criticisms can all be made against even the most human of humans. Does it mean humans have no meaning or understanding? No.

Of course these are also conditional on whatever prompt you are putting to get them to answer questions. If you prompt an advanced raw GPT-N to make stupid mistakes and factual and logical errors and to act especially toxic then it will do it. And, perhaps, only then will it have truly attained meaning and understanding.