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Comment by suddenlybananas | Hacker News Reader

Don't try and say anything pro-linguistics here, people are weirdly hostile if you think it's anything but probabilities.

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Over my years in academia, I noticed that the linguistics departments were always the most fiercely ideological. Almost every comment of a talk would be get contested by somebody from the audience.

It was annoying, but as a psych guy I was also jealous of them for having such clearly articulated theoretical frameworks. It really helped them develop cohesive lines of research to delineate the workings of each theory

Are there really all that many parallels between linguistics (the study of langauge) and computational-linguistics/NLP (subject of discussion)?

Computational linguistics, yes - it is the application of linguistics to computers.

Modern NLP, not really - it's all based around statistical modeling with very little linguistics.

IME, computational linguistics is just NLP and has been for 10 years or so.

Maybe they could’ve tried to say something pro-linguistics, but the comment was entirely anti-LLM.

God forbid someone not bow before the almighty LLM

> Don't try and say anything pro-linguistics here, (...)

Shit-talking LLMs without providing any basis or substance is not what I would call "pro-linguistics". It just sounds like petty spiteful behavior, lashing out out of frustration for rendering old models obsolete.

From a scientific explanatory perspective, the old models are not obsolete because they are explanatory whereas LLMs do not explain anything about human linguistic behaviour.

On one hand you have models which you argue are explanatory, but arguably do not work.

On the other hand, you have models that not only work but took the world by storm, and may or may not be made explanatory.

You either invest more work getting one explanatory model to work, or invest more work getting a working model to become explanatory.

What do you think is the fruitful research path?

What do you mean "work"? Their goal is not to be some general purpose AI. (To be clear I'm talking narrowly about computational linguistics, not old fashioned NLP more broadly).

The interesting question is whether just a gigantic set of probabilities somehow captures things about language and cognition that we would not expect ...

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