3 ms·

> I would have thought this would be a super easy task for it Why did you think that? This isn't meant to be critical, but I'm honestly curious, what led yo

> I would have thought this would be a super easy task for it Why did you think that? This isn't meant to be critical, but I'm honestly curious, what led you to believe that technology underlying GPT-4 made it a good fit for this or any particular task?

londons_explore 3y ago
I too would think it a super easy task. It has probably seen the correct nouns used millions of times in the training data - and asking it to produce the correct nouns for a bunch of words is really just "tell me which case you saw most during training", which is something LLM's are really good at.
- pmoriarty 3y ago
  From what I understand, it's trained mostly on English, and performs better in English. So it's not at all surprising it would make more mistakes in German or some other language.
constantcrying 3y ago
>Why did you think that? It is a purely statistical model. It does not know any "rules" about the language (it doesn't know any language at all), but it is fed data and derives from that sophisticated probabalistical relationships between words. It shouldn't have much of a problem to generate the correct grammatical formulations, as it has been extensively trained on them. Moreso than any other technology neural networks are suited for this kind of tasks where hard rules do not exist (as a German I couldn't tell you why "rain" is masculine, but "machine" is feminine), but lots of data correctly implements the rule, does.
- Trapais 3y ago
  I have doubts it was extensively trained on German data. Who knows about GPT4, but GPT3 is ~92% of English and ~1.5% of German, which means it saw more "die, motherfucker, die" than "die Mutter". (https://github.com/openai/gpt-3/blob/master/dataset_statistics/languages_by_word_count.csv https://github.com/openai/gpt-3/blob/master/dataset_statisti...)

3 ms·

> I would have thought this would be a super easy task for it Why did you think that? This isn't meant to be critical, but I'm honestly curious, what led yo

by akira2501 3y ago

londons_explore 3y ago
I too would think it a super easy task. It has probably seen the correct nouns used millions of times in the training data - and asking it to produce the correct nouns for a bunch of words is really just "tell me which case you saw most during training", which is something LLM's are really good at.
- pmoriarty 3y ago
  From what I understand, it's trained mostly on English, and performs better in English. So it's not at all surprising it would make more mistakes in German or some other language.
constantcrying 3y ago
>Why did you think that? It is a purely statistical model. It does not know any "rules" about the language (it doesn't know any language at all), but it is fed data and derives from that sophisticated probabalistical relationships between words. It shouldn't have much of a problem to generate the correct grammatical formulations, as it has been extensively trained on them. Moreso than any other technology neural networks are suited for this kind of tasks where hard rules do not exist (as a German I couldn't tell you why "rain" is masculine, but "machine" is feminine), but lots of data correctly implements the rule, does.
- Trapais 3y ago
  I have doubts it was extensively trained on German data. Who knows about GPT4, but GPT3 is ~92% of English and ~1.5% of German, which means it saw more "die, motherfucker, die" than "die Mutter". (https://github.com/openai/gpt-3/blob/master/dataset_statistics/languages_by_word_count.csv https://github.com/openai/gpt-3/blob/master/dataset_statisti...)