Sorry, but this is simply incorrect. Do you know what Eliza is and how it works? It is categorically different from LLMs.

I did not mean to come across as stating that they were the same, nor that the results produced would be as good. Merely, that a PDF could be run through OCR and processed into a script for ELIZA, which could produce some results to requests for a summary (ex. provide the abstract).

My point being that these technologies that are fundamentally different and at very different levels of technological sophistication can both, at a high level, accomplish the task. Both the quality of the result and capabilities beyond the surface level are very different. However, both, would be able to produce one, working within their architectural constraints.

Looking at it this way also gives a good basis for comparing LLMs to intelligence. Both, at a high level, can accomplish many of the same tasks, but, context matters in more than a syntactical sense and LLMs lack the capability of understanding and comprehension of the data that they are processing.

This is also incorrect.

That paper is both solely phenomenological and states that it is not using an accepted definition of intelligence. With the former point, there's a significant risk of fallacy in such observation as it is based upon subjective observation of behavior not emperical analysis of why the behavior is occuring. For example leatherette may approximate the appearance and texture of leather but, when examined it differs fundamentally both on the macroscopic and microscopic level, making it objectively incorrect to call it "leather".

I think the issue that many people have is that they hear "AI" and think "superintelligence". What we have right now is indeed AI. It's a primitive AI and certainly no superintelligence, but it's AI nonetheless.

Here, we're really getting into semantics. As the authors of that paper noted, they are not using a definition that is widely accepted, academically. Though they do definitely have a good point on some of the definitions being far too anthropocentric (ex. "being able to do anything that a human can do" - really, that's a shit definition). I would certainly agree with the term "primitive AI", if used akin to programming primitives (int, char, float, etc.) as it is clear that LLMs may be useful components in building actual general intelligence.