[xcv123]

A human is an ape that is obeying patterns that it has been trained on. What is school but a bunch of apes being trained to obey patterns? Some of these apes do well enough to convince you that it understands things. Some apes fully "understand" that flat earth theory is true, or they "understand" that the Apollo moon landings were faked.

You have a subjective philosophical disagreement about what constitutes understanding. That is fine. I clearly understand it is not conscious and that programs do not understand things the way that humans do. We are fundamentally different to LLMs. That is obvious. But you are not making a technical argument here unless you can define "understand" in technical terms. This is a matter of semantics.

> It is a token manipulation machine

Deep learning and machine learning in general is more than token manipulation. They are designed for pattern recognition.

[Guvante]

You acknowledged above that consciousness isn't what LLM is and you likely understand that the poster was referring to that...

The broad strokes you use here are exactly why discussing LLMs are hard. Sure some people dismiss them because it isn't general AI but having supporters dismiss any argument with "passes the Turning test" is equally useless.

[xcv123]

No you have misunderstood. As I wrote above:

"But you are not making a technical argument here unless you can define "understand" in technical terms. This is a matter of semantics."

I said the nature of their argument is not technical, since they are not dealing with technical definitions, but I did not dismiss their argument altogether. I clarified and restated their own argument for them in clearer terms. LLMs are not conscious, but they can still "understand" very well depending on your definition of understand. Understanding is not a synonym for consciousness. Language is evolving and you need to be more precise when discussing AI / machine learning.

One definition of understand is:

"perceive the intended meaning of (words, a language, or a speaker)."

Deep learning models recognize patterns. Mechanical perception of patterns. They understand things mechanically, unconsciously.

[Guvante]

I stand by my point that people using synonyms for consciousness being told "LLM knows true better than humans do" is bad for discussion.

The core issue is their "knowledge" is too context sensitive.

Certainly humans are very context sensitive in our memories but we all have something akin to a "mental model" we can use to find things without that context.

In contrast LLM has knowledge defined by that context quite literally.

In either case my original point on using true and false is that LLM can hallucinate and on a fundamental design level there is little that can be done to stop it.

[xcv123]

LLMs can outperform humans on a variety of NLP tasks that require understanding. Formally, they are designed to solve "natural language understanding" tasks as a subset of "natural language processing" tasks. The word "understanding" is used in the academic context here. It is a standard term in NLP research.

https://en.wikipedia.org/wiki/Natural-language_understanding

My point was to show that their thinking, reasoning and language was flawed, that it lacked nuance and rigor. I am trying to raise the standards of discussion. They need to think more deeply about what "understanding" really means. Consciousness does not even have a formal universally agreed definition.

Sloppy non-rigorous shallow arguments are bad for discussion.

> LLM can hallucinate and on a fundamental design level there is little that can be done to stop it.

That's a separate issue. They generally don't hallucinate when solving a problem within their context window. Recalling facts from their training set is another issue.

Humans sometimes have a similar problem of "hallucinating" when recalling facts from their long term memory.

[Guvante]

Except that if you narrow to a tiny training set you are back to problems that can be solved almost as quickly with full text search...

[xcv123]

Narrow to a tiny training set? What are you talking about now? That has nothing to do with deep learning.

GPT-3.5 was trained on at least 300 billion tokens. It has 96 layers in its neural network of 175 billion parameters. Each one of those 96 stacked layers has an attention mechanism that recomputes an attention score for every token in the context window, for each new token generated in sequence. GPT-4 is much bigger than that. The scale and complexity of these models is beyond comprehension. We're talking about LLMs, not SLMs.