There was some research showing that training a model on facts like "the mother of John Smith is Alice" but in German allowed it to answer questions like "who's the mother of John Smith", but not questions like "what's the name of Alice's child", regardless of language. Not sure if this holds at larger model sizes though, it's the sort of problem that's usually fixable by throwing more parameters at it.
Language models definitely do generalize to some extend and they're not "stochastic parrots" as previously thought, but there are some weird ways in which we expect them to generalize but they don't.
Do you have any good sources that explain this? I was always thinking LLMs are indeed stochastic parrots, but language (that is the unified corpus of all languages in the training data) already inherently contains the „generalization“. So the intelligence is encoded in the language humans speak.
The mental gymnastics required to handwave language model capabilities are getting funnier and funnier every day.
The most famous result is OthelloGPT, where they trained a transformer to complete lists of Othello moves, and the transformer generated an internal model of where the pieces were after each move.
The rough consensus is that if you train a model to predict the output of a system for long enough with weight decay and some nebulous conditions are met (see "lottery ticket hypothesis"), eventually your model develops an internal simulation of how the system works because that simulation uses fewer weights than "memorize millions of patterns found in the system", and weight decay "incentivizes" lower-weight solutions.
Performance improved across all benchmarks; in English (the original language).
https://openreview.net/forum?id=KIPJKST4gw
Is symbolic language a fuzzy sort of code? Absolutely, because it conveys logic and information. TLDR: yes!
IE you train an LLM on both English and French in general, but only teach it a specific fact in French, it can give you that fact in English
Still blows my mind we came so far so fast.