5 ms·

Yes it can only learn to solve the problems that it was trained to solve. Not surprising really. Tautological.

• ngneer 3y ago
  Right, but that ignores the key piece, which is whether such systems can infer the equivalence relation denoted by "is". A is A. A is B implies B is A. A is B and B is C implies A is C. When a system sees that A is B, yet cannot infer that B is A, it exhibits an asymmetry, which is interesting. Whether this asymmetry exists in the underlying language is unclear.
  • xcv123 3y ago
    > When a system sees that A is B, yet cannot infer that B is A So the issue is that they cannot infer that general rule due to a fundamental limitation of the transformer LLM architecture, not just a training data issue? I skimmed the paper and it seems to be the case.
  • dr_kiszonka 3y ago
    > A is B implies B is A. I get the overall idea, but this statement isn't always true, right?
    • xcv123 3y ago
      Correct. Not in natural language. These are natural language systems, not logic systems. "The sky is blue" does not imply "blue is the sky".
      • TeMPOraL 3y ago
        There's an asymmetry here, too. "The sky is ${color}", with no extra context, has one obvious answer for us living here, today. Whereas for "Blue is the color of ${thing}", with no extra answer, there's an insane amount of equally sensible substitutions for ${thing}. Without extra content, the model has no reason to privilege "sky" over any other equally valid answer.