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I think the lack of explicit linguistic tools is the key to success, forcing/enabling the generic model to learn implicit linguistic tools (there's some researc
by PeterisP 4y ago
I think the lack of explicit linguistic tools is the key to success, forcing/enabling the generic model to learn implicit linguistic tools (there's some research identifying that analysis of specific linguistic phenomena happens at specific places in the NN layers) that work better than what we could implement.
"It would be interesting to know how different a model would be if it operated on eg dependency trees instead of the linear list of tokens." - indeed, this is obviously interesting, so people have tried that a lot for many models, but IMHO it's probably now almost decade since the consensus is that in general end-to-end training (once we became able to do it) work better than adding explicit stages in between, e.g. for any random task I would expect that doing text->syntax tree->outcome is going to get worse results than text->outcome, because even if the task really needs syntax, the syntax representation that a stack of large transformer layers learns implicitly tends to be a better representation of the natural language than any human linguist devised formal grammar, which inevitably has to mangle the actual languge to fit into neat human-analyzable 'boxes'/classes/types in which it doesn't really fit and all the fuzzy edge cases stick out. Once you remove the constraint that the grammar must be simplified enough for a human to be able to rationally analyze and understand, and (perhaps even more importantly?) abandoning the need to prematurely disambiguate the utterance to a single syntax tree instead of accepting that parts of it are ambiguous (but not equally likely), processing works better.
It's just one more reminder of the bitter lesson (http://incompleteideas.net/IncIdeas/BitterLesson.html http://incompleteideas.net/IncIdeas/BitterLesson.html) which we don't want to accept.
- deleted 4y ago[deleted]
- clementneo 4y agoI think there's probably some truth to this. They found that in InstructGPT — where they teach the model to better follow instructions, which was the jump from GPT-3 to ChatGPT — they found that the model also learnt to follow non-English instructions, even though the extra training was done almost exclusively in English[1]. So there seems to be such emergent mechanisms in the model that have arisen because of the end-to-end training, which we don't exactly understand yet. [1] https://twitter.com/janleike/status/1625207251630960640 https://twitter.com/janleike/status/1625207251630960640
- kilgnad 4y agoHmm I think it's sort of a hybrid. First off, we know that overall the concept of language is humans is an emergent phenomenon. It developed from natural selection from simple components so there's validity that the same thing can occur in an LLM where some overarching emergent structure develops from simple primitives. At the same time we do know that a sort of universal grammar exists among humans. Our language capacity is biased in a certain way and that it is unlikely for it to learn languages of a very extreme and divergent grammar from the universal one discovered by Noam chomsky. That means our brain is unlikely to be as universally simple as an LLM. I think the key here is that the human mind has explicit linguistic tools but the these tools are still emergent in nature.
- vajrabum 4y agoLots of linguistics researchers would disagree about the existence and necessity of universal grammar or Chomsky's 'language acquisition device' and in fact the success of LLMs and statistical models which very clearly have no LAD and no universal grammar over feature engineering or grammar based schemes suggests the opposite.
- kilgnad 4y agoI'm outdated on this research. I took a linguistics class in UCLA about a decade ago and at the time universal grammar was the prevailing norm. Has it changed since then? Have they found a significant number of human civilizations that use divergent grammars?
- solarmist 4y agoChomsky’s universal grammar is a modification of Harris’s Operator grammar. Harris’s operator grammar was based on set theory however Chomsky was enamored with formal logic and ran in that direction. Also, Chomsky became famous while Harris didn’t. Operator grammar is self-discoverable and he published a full description of English grammar in the 1980’s using this theory and an extension in the 1990’s which generalized to other languages. It is not fully deterministic (final word selection and ordering is probabilistic), but it is much more convincing to me and in line with how we understand brains to work. Over the last 20 years Chomsky has begun to fall out of favor because it’s just so complicated and requires external structures, etc.