[nl]

> No one here is a scientist and no one treats any of this as science. Where's the criteria for the emprical adequecy of NLP systems as models of language? Specifying any, conducting actual hypothesis tests, and establishing a theory of how NLP systems model language -- this would immediately reveal the smoke-and-mirros.

What do you mean?

I'm not a scientist but I play one sometimes, and I managed a whole team of them working in this field.

The theory of language models is well established.

> Where's the criteria for the emprical adequecy of NLP systems as models of language?

There are lots(!?) I think the Winograd schema challenge[1] is an easy one to understand, and meets a lot of your objections because it is grounded in physical reality.

Statement:

The city councilmen refused the demonstrators a permit because they [feared/advocated] violence.

Question:

Does "they" refer to the councilmen or the demonstrators?

The human baseline for this challenge is 92%[1]. PaLM (this Google language model) scored 90% (4% higher than the previous best)[3].

[1] https://en.wikipedia.org/wiki/Winograd_schema_challenge

[2] http://ceur-ws.org/Vol-1353/paper_30.pdf

[3] https://storage.googleapis.com/pathways-language-model/PaLM-... pg 12

[mjburgess]

Indeed, all these test are not of empirical adequacy which really evidences the point. The whole field is in this insular pseudoscientific mould of "its true if it passes an automated test to x%".

A theory with empirical adequecy would require you to do some actual research into language use in humans; all of its features; how it works; various theories of its mechanisms etc. And after a comprehensive, experimental and detailed theoretical work -- show that NLP models even *any* of it.

Ie., that any NLP model is a model of language.

All you do above is design your own win condition, and say you've won. This precludes actually knowing anything about how language works, and is profoundly pseudoscientific. If you set-up tests for toys, and they pass -- good, you've made a nice toy.

You may only claim is models some target after actually doing some science.

[nl]

A theory with empirical adequecy would require you to do some actual research into language use in humans; all of its features; how it works; various theories of its mechanisms etc. And after a comprehensive, experimental and detailed theoretical work -- show that NLP models even any* of it.*

What - specifically - do you mean?

There's an entire field adjacent to NLP called Computational Linguistics. Most people in the field work across them both, and there is significant cross pollination.

It's unclear if think there is some process in the brain that you think NLP models should be similar to. If this is the case you should look at studies similar to [1] where they do MRI imaging and can see similar responses in semantically similar words. This is very similar to how word vectors put similar concept closely together (and of course how more complex models put concept close together).

Or perhaps you think that NLP models do not understand syntactic concepts like nouns, verbs etc. This is incorrect too[2].

[1] https://www.tandfonline.com/doi/full/10.1080/23273798.2017.1...

[2] https://explosion.ai/demos/displacy

[mjburgess]

It should do what language does...

Language is a phenomenon in, at least, one type of animal. It allows animals to coordinate with each other in a shared environment; it describes their internal and external states; etc. etc.

Language is a real phenomenon in the world that, like gravity, can be studied. It isnt abstract.

NLP models of language arent models of language. Theyre cheap imitations which succeed only to fool language users in local highly specific situations.

[nl]

> NLP models of language arent models of language.

Do you actually know what a NLP Language Model refers to? It literally is a model of the language - it predicts the likelihood of the next word(s) given a set of prior word(s).

It seems you think people just throw some data at a neural network and then go wow. It's not like that at all - the field of NLP grew out of linguistics study and has deep roots in that field.

[mjburgess]

That's not a model of language. Language is a communicative activity between language users, who do things with words, with each other.

What you're talking about is ignoring the entire empirical context of langauge, as a real-world phenomenon, and modelling is purely formal characteristics as recorded post-facto.

This will always just produce a system which cannot use langauge, but will only ever appear to within highly constrained -- essentially illusory -- contexts. Its the difference between a system which makes a film by "predicting the next frame", and a making a film by recording actual events that you are directing.

A prediction of a "next frame" is always therefore just going to be a symptom of the frames before it. When I point a camera at something new, eg., an automobile in c. 1900 -- i will record a film that has never been recorded before.

And likewise, with words: we are always in genuinely unquie unprecedented situations. And what we *do with words*, is speak about those situations *to others* who are in them with us... we aim to coordinate, move, and so on with words.

To model *language* isnt to model words, nor text, nor to predict words or text. It is to be a speaker here in the world with us, using language to do *what language does*.

No model of the regularities of text will ever produce a language-user. Language isnt a regularity, like the frames of a film -- its a suit of capacities which are responsive to the world, and enable language users to navigate it.

[nl]

Until you can make quantifiable predictions of behaviour that you want to see it sounds like your objections are philosophical rather than scientific.

> A prediction of a "next frame" is always therefore just going to be a symptom of the frames before it.

But the physical appearance of the automobile itself was absolutely influenced by what went before - they were called "horseless carriages" after the appearance after all.

And NLP Language Models can produce genuinely original and unique writing. This is a poem a large LM wrote for me:

  The sceptered isle
  Hath felt the breath of Britain,
  Longer than she cares to remember.
  Now are her champion arms outstared,
  Her virgin bosom stained with battle's gore.
  Lords and nobles, courtiers and commons,
  All stand abashed; the multitudinous rout
  Scatter their fears in every direction;
  Except their courage, which, to be perfect,
  Must be all directed to the imminent danger
  Which but now struck like a comet; and they feel
  The blow is imminent

> we aim to coordinate, move, and so on with words.

https://say-can.github.io/

"Robots ground large language models in reality by acting as their eyes and hands while LLMs help robots execute long, abstract language instructions"

[FeepingCreature]

> No model of the regularities of text will ever produce a language-user.

No but it will produce language-users, incidentally. Language-users are an irreducible aspect of the underlying regularity in language. Now I'm not saying that "GPT will wake up" purely from language tasks, that GPT will become a language user by being a system that picks up regularities. But for GPT to contain systems like language users, to instantiate language-users, which it has to (on some level) in order to successfully predict the next frame, is already enough to be threatening.

I know that using examples from fiction is annoying, but - purely as a rhetorical aid - consider the Enterprise computer (in Elementary, Dear Data) as GPT, and the Moriarty hologram as an embedded agent. The Enterprise computer is not conscious, but as a very powerful pattern predictor it can instantiate conscious agents "by accident", purely by completing a pattern it has learnt. It doesn't want to threaten the Enterprise, it doesn't want to not threaten the Enterprise, because it doesn't have any intentional stance. Instead, it was asked "A character that can challenge Data is ¬" and completed the sentence, as is its function.