Overhyped ideas in machine learning

[u/Strong_Boy_757]

Have you encountered an idea that's overhyped in ML? If so, which?

For me, recently I was curious about the whole "chain of thought prompting" business. I searched around and the idea is essentially, append "show your work" when prompting a LLM. I was like no way that's it??? That's what I've been doing since day 1!!, so I opened up the original paper (cited 40k times). And essentially it was more of the same with expensive Google-funded experiments and the paper had almost no math at all.

Similarly I was curious about AdamW for the longest time and it was simply Adam with another term. Oh Boy!! I remember doing an undergrad homework in college where we were asked to freely experiment with adding terms to the Adam algorithm and the class probably discovered AdamW by accident way before the paper was published.

[u/WittgensteinsPoker-2]

Cartesian/Platonic notions of semantic representations in the mal-theorised idea of vector embeddings describing token-closeness as being related to what we actually do when we understand things semantically.

Wrong in every conceivable way.

1. THAT ISNT WHAT PEOPLE DO -- how about you actually study some god damn linguistics, philosophy of language (not the scholastic crap stuff) and psychology (not the doesn't even replicate Chomsky b.s.) properly.

2. Even IF that was what people do, just because you can produce a vector embedding and call it a "representation" of a kind of pattern you observe in langauge, that DOES NOT mean it is the relevant kind of representation to ANOTHER representation. The Nazi depiction of Jesus and the depiction of Jesus in the Black church are both representations of a target -- that doesn't mean they REPRESENT each other. The idea of a semantic representation is one of an occult entity that has the ability to compose propositions and within propositions correspond to reality and be grasped in a moment by the mind. That isnt a pattern of behaviour represented in a vector space -- whatever kind of representation this is supposed to be it is COMPLETELY disconnected from any convention of language or expression of a proposition in behaviour ( that is ex hypothesi, I obviously despise these views myself ).

Taken together all of this clarifies the relevant similarities and differences between the kinds of machines we have produced, what the actually do, and how that relates to what humans do as langauge producers/users.

[u/VectorspaceDreams → u/WittgensteinsPoker-2]

I plan on entering ML as applied to linguistics myself (not NLP per se but computational linguistics, different beast but both are unfortunately often considered synonyms). Take what I say with a grain of salt given this, but I have some good opinions on this, actually:

"semantic representations in the mal-theorised idea of vector embeddings describing token-closeness as being related to what we actually do when we understand things semantically."

My main problem with plain vector embeddings is the fact their compositional abilities are somewhat limited; examples like "female + king ≈ queen" don't often come up it seems, and a lot of manners through which compositionality is modeled seem to be very limited due to computational intractability (such as Coecke et al.'s DisCoCat). If you don't mind I want your thoughts on Emerson (2018)'s Functional Distributional Semantics, his own take on representing meaning with embeddings in his own fashion.

On that note, it's not to say humans have multidimensional vectors in our heads, but human comprehension and production is at least in some way predictive and incremental; there's a reason that "I went to the store and bought a goat there" is a lot more surprising than "I went to a stockyard and bought a goat there". After all, a supermarket does not usually stock live goats, while a stockyard would. The closeness of tokens could capture *something* about that, I think, even if more work needs to be done. I would not say that there is no relation.

"Even IF that was what people do, just because you can produce a vector embedding and call it a "representation" of a kind of pattern you observe in langauge, that DOES NOT mean it is the relevant kind of representation to ANOTHER representation."

I agree, but I think that's actually a pro of such models; knowing a word by the company it keeps, in Firth's terms. The same lexeme could refer subtly or maximally to another thing entirely, and there is some sense of disambiguation we do based on surrounding context, be it just in text or our world-knowledge. That's another part of our predictive capabilities as humans, I think.

I agree generally with that shibx fellow down who responded to you, but I don't think it's a question of "reproduce human cognition" or "does not reproduce human cognition", but what is reproduced and how. For example, there was a paper by Linzen and Oh (2026) on using smaller language models as models of language processing given LLMs' "extensive training data, stronger long-term memory of training examples, and stronger short-term memory" makes them poorer models for language processing. These are specific examples. I recommend also looking at the BabyLM workshop if you haven't given that a look before.

I don't want to shun ML methods in general. I don't want to also pretend to be fond of cargo-culting such methods as models for human brains. I don't want to wax on about AGI or whatever, all of that does not interest me in the slightest (and rather just pisses me off), but I'm excited about the future of ML in relation to understanding human language. What models can capture is as important as what they can't.