[xcv123]

> They have no ability to gain knowledge out of learned text, just counting occurences of words in texts and giving them a weight, depending on the relationship in that text. They are just putting combinations of text together.

No, they use deep neural networks to build a hierarchical semantic model. They are not simple occurrence counters.

Also the current state of the art of LLMs handles negation easily. This article is outdated.

Here's an example from https://openai.com/research/language-models-can-explain-neur...

"Seriously, you guys. I think I found the Mobile Leprechaun from '06. He's been hiding right in front of our eyes."

Token: hiding

layer 0: “verbs in gerund form (ending in 'ing')”

layer 2: “words related to hiding, concealment, or enclosed spaces”

layer 4: “words related to mental states, particularly anxiety and stress”

layer 17: “words and phrases related to silence or quietness”

[somewhereoutth]

All that can be supplied to a LLM for training is syntax. There is no way to provide semantics, it only understands 'table' in regard to syntax it has already seen including that particular token. It has no experience and therefore no understanding of a real table.

It may internally construct a hierarchy as you set out, but this is and can only be a syntactical hierarchy - though should be no surprise that it corresponds to our usual semantic hierarchy. But whereas our syntax proceeds from our semantics, its syntax proceeds only from our syntax that we've fed it.

[xcv123]

That's a philosophical issue, not technical.

No one is saying these models are conscious or have human awareness of concepts.

It mechanically builds a deeply layered semantic model that correlates to our human understanding.

Quibbling over whether it is "real semantics" or not is just ironically quibbling over semantics. Yes its not conscious, but it doesn't need to be. It is possible to build a mechanical structure that correlates to a human understanding of the world and performs useful tasks that require only mechanical understanding and reasoning, without consciousness or emotions.

[somewhereoutth]

The distinction between semantics and syntax is pretty tight, no philosophy required. The former considers the domain being represented, whereas the latter is strictly the symbols used in the representation.

So to be precise it mechanically builds a deeply layered syntactic model. LLMs just regurgitate syntax, any semantics can only be imagined by us and overlaid on the syntactic results produced.

[xcv123]

You are disagreeing philosophically about what constitutes "true" semantics. That is not a technical argument.

If you are right about this, then you should edit or delete this wikipedia article and publish a paper to inform all NLP researchers that there is no such thing as a semantic similarity metric because NLP models cannot understand "true" semantics.

https://en.wikipedia.org/wiki/Semantic_similarity

[pmoriarty]

Does a definition of a word in a dictionary provide the syntax or the semantics of the word it defined?

[somewhereoutth]

In common use by humans, it provides semantics, in terms of other previously understood semantics. However, if there is no semantic understanding by the reader, then all it provides is a syntactic rewrite rule for each word.