7 ms·

I don't get it. A is B doesn't always mean B is A.

by tomohelix 3y ago

ngneer 3y ago
Could you give an example?
- warkdarrior 3y ago
  Cows are mammals, but mammals are not cows.
  - sam_goody 3y ago
    "Cows are mammals" means: Array($mammals).includes($cows). It does not mean $cows === $mammals. "A is a B" Or "A's are B's" means that A is included in B. $a === $b ("A is B") implies that $b === $a (B is A), in all cases that I know of.
    - ToValueFunfetti 3y ago
      What cases are there for $a === $b? I guess there's synonyms and rephrasing, but in general it's not a useful relationship in language
      - wizzwizz4 3y ago
        Batman is Bruce Wayne.
      - xcv123 3y ago
        From this paper: https://owainevans.github.io/reversal_curse.pdf https://owainevans.github.io/reversal_curse.pdf "In particular, suppose that a model’s training set contains sentences like “Olaf Scholz was the ninth Chancellor of Germany”, where the name “Olaf Scholz” precedes the description “the ninth Chancellor of Germany”. Then the model may learn to answer correctly to “Who was Olaf Scholz? [A: The ninth Chancellor of Germany]”. But it will fail to answer “Who was the ninth Chancellor of Germany?” and any other prompts where the description precedes the name."
    - akomtu 3y ago
      The operator are, which happens to look similar to an english word, has a meaning that depends on context. Another example is 5/2 means a simple fraction in one context, or a class of polytopes in another.
PoignardAzur 3y ago
GP meant that LLMs trained to learned "A is a B" won't necessarily be able to answer "give an example of B". Eg given the fact "Foo is the capital city of Bar", it might be able to complete "Foo is the capital city of _" but not "The capital city of Bar is _". (Which isn't necessarily a evidence against truth models for out-of-distribution facts, it could just be a matter of indexing.)
- xcv123 3y ago
  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".

I don't get it. A is B doesn't always mean B is A.

ngneer 3y ago

Could you give an example?

warkdarrior 3y ago
Cows are mammals, but mammals are not cows.
- sam_goody 3y ago
  "Cows are mammals" means: Array($mammals).includes($cows). It does not mean $cows === $mammals. "A is a B" Or "A's are B's" means that A is included in B. $a === $b ("A is B") implies that $b === $a (B is A), in all cases that I know of.
  - ToValueFunfetti 3y ago
    What cases are there for $a === $b? I guess there's synonyms and rephrasing, but in general it's not a useful relationship in language
    - wizzwizz4 3y ago
      Batman is Bruce Wayne.
    - xcv123 3y ago
      From this paper: https://owainevans.github.io/reversal_curse.pdf https://owainevans.github.io/reversal_curse.pdf "In particular, suppose that a model’s training set contains sentences like “Olaf Scholz was the ninth Chancellor of Germany”, where the name “Olaf Scholz” precedes the description “the ninth Chancellor of Germany”. Then the model may learn to answer correctly to “Who was Olaf Scholz? [A: The ninth Chancellor of Germany]”. But it will fail to answer “Who was the ninth Chancellor of Germany?” and any other prompts where the description precedes the name."
  - akomtu 3y ago
    The operator are, which happens to look similar to an english word, has a meaning that depends on context. Another example is 5/2 means a simple fraction in one context, or a class of polytopes in another.

PoignardAzur 3y ago

GP meant that LLMs trained to learned "A is a B" won't necessarily be able to answer "give an example of B". Eg given the fact "Foo is the capital city of Bar", it might be able to complete "Foo is the capital city of _" but not "The capital city of Bar is _". (Which isn't necessarily a evidence against truth models for out-of-distribution facts, it could just be a matter of indexing.)

xcv123 3y ago
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".