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Large language models are poor theories of human linguistic cognition
- richardjam73 4y ago"The distinction between likely and grammatical, which all humans have, is entirely foreign to ChatGPT and its fellow LLMs." Because grammar is not determined by frequency.
- adastra22 4y agoSure it is. Something is grammatical because you have frequently in the past heard sentences constructed that way. Something is ungrammatical because you never heard sentences constructed that way. It’s purely based on frequency.
- adammarples 4y agoThis is wrong. "Colorless green ideas sleep furiously" is a famous example proposed as a clearly grammatical but meaningless, entirely new and unlikely sentence. There are many grammatically correct sentences you will hear in future that you've never heard before.
- brokenkebaby 4y agoSorry, but what is wrong then? The example is grammatically correct, because it fits established pattern, i.e. you may have never heard that exact same phrase, but you definitely heard multitude of phrases exhibiting the same pattern/rules
- richardjam73 4y agoLLMs have been exposed to much larger datasets than any human ever has. If it was frequency then humans would make the grammatical errors and not the LLMs. The LLMs are making grammatical errors as shown in the article and therefore it is not about frequency.
- brokenkebaby 4y agoHumans do grammatical errors (non-native, for whatever expressive effect, typos, dialects, slangs etc.) => datasets contain a percentage of grammatical errors => LLMs does it. I mean it can be about frequency, and carry infrequent errors because of it. I don't see any contradiction.
- adastra22 4y agoThat’s entrance fits what I said just fine. The words are arranged in the order that I have frequently seen in the past, so I accept it as grammatical (albeit meaningless).
- adammarples 4y agoYou've seen those words in that order in the past? Then pick an example you haven't seen (orange ideas etc)
- karmakaze 4y ago> One central factor behind this failure is the learning method: LLMs are trained using backpropagation, which pushes the network in very un-scientist-like directions and prevents it from generalizing and reasoning about inputs in anything even remotely similar to how humans generalize. It's hard to take this backpropogation dis as fact given the performance and progression of AlphaGo, AlphaGoZero, AlphaZero, MuZero. > When instead of backpropagation we trained networks using Minimum Description Length (MDL) — a learning criterion that does correspond to rational scientific reasoning — the networks were able to find perfect solutions to patterns that remained outside the reach of networks trained with backpropagation. Could there eventually be future LLMs (MDL-based or otherwise) that would strike us as scientist-like? Perhaps. But if such models do arrive they will be very different from current LLMs. In the meantime, any suggestion that LLMs are automated scientists should be treated with suspicion. Seems like the critique is specifically about how current LLMs operate and not about them in principle.
- puttycat 4y ago> Seems like the critique is specifically about how current LLMs operate and not about them in principle. Yes, this is written explicitly in the paper.
- maxdoop 4y agoThe author assumes that asking GPT to “take longer and reconsider” is something that an LLM actually does. As in, temporally. The author claims that “even after taking longer to reconsider” an answer, ChatGPT was still wrong. The author appears to misunderstand how LLMs work.
- leobg 4y agoSounds a bit like Chomsky’s critique of Skinners “Verbal Behavior”, which kind of put him on the map back in the 60s. Though Skinner gave us teaching machines, ping pong playing pigeons, clicker training and, indirectly, ChatGPT.