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How does chat-gpt actually get this right? This would appear to require some degree of reasoning, but as far as i understand its output is purely probabilistic,

by retube 3y ago

How does chat-gpt actually get this right? This would appear to require some degree of reasoning, but as far as i understand its output is purely probabilistic, based on existing corpus of text.

1270018080 3y ago
You answered your own question.
- 15457345234 3y ago
  Can you elaborate?
  - jw1224 3y ago
    Your question: > How does chat-gpt actually get this right? Your answer: > its output is purely probabilistic, based on existing corpus of text Because GPT was trained on existing text, some of which included numbers and counting, it's learnt the natural ordering of most common/everyday numbers. For larger or more complex numbers, it's learnt the patterns behind how they're constructed linguistically, which allows it to output a written count in sequence. This same pattern recognition doesn't work anywhere near as well for actual numerals (e.g. "47600", instead of "forty seven thousand six hundred"), as the tokenizer tends to break long numerals apart (e.g. into ["476", "00"]).
    - pbhjpbhj 3y ago
      In text, we don't often count in series, and it seems likely that we often choose a non-counting sequence: like 'I chose options 1, 2, 7' or 'my code was 0 1 2 5', whatever. Unless training included line-level skips, rather than just next-word skips (like word2vec) or concept-level associations? At the line level, or paragraph level, ordered numerical sequences are obviously very common in formal texts or in code. I've seen sentence based training, I suppose for code (which it seems GPT4 excells at) line-level training would be essential. Anyone recommend a mid-level read on this covering different modes of training and such; I'm happy with a bit of code and undergrad level maths. Thanks.
      - jw1224 3y ago
        > Unless training included line-level skips Yes, of course — GPT-4 was trained on all common character sequences, including linebreaks and other invisible characters. You can see how it works here: https://platform.openai.com/tokenizer https://platform.openai.com/tokenizer Nonetheless it doesn't need to have seen examples of line-level counting before. The "concept-level associations" you mentioned are an emergent property of the model, it forms its own concept-level associations as a result of being trained on such a massive dataset. It's what enables it to output original content which has never been seen before.
      - Zambyte 3y ago
        Maybe we count in series a lot more than you think. https://www.youtube.com/watch?v=WO2X3oZEJOA https://www.youtube.com/watch?v=WO2X3oZEJOA
pyinstallwoes 3y ago
Aren't your sentences probabilistically based on the existing corpus of text within your mind?
- 15457345234 3y ago
  No? If that's how sentence forming worked it would be impossible to count backwards, or count at all for that matter.
  - pyinstallwoes 3y ago
    Why? Numbers have directionality relative to the species.
  - stavros 3y ago
    Me: Count backwards from ten GPT-4: 10, 9, 8, 7, 6, 5, 4, 3, 2, 1 Me: Count backwards from a million to 999990 GPT-4: 1,000,000, 999,999, 999,998, 999,997, 999,996, 999,995, 999,994, 999,993, 999,992, 999,991, 999,990 I don't know about that.
    - 15457345234 3y ago
      I don't know how that connects in any way to what I and the commenter preceding me wrote.
      - stavros 3y ago
        You said that sentences aren't formed by probability from an existing corpus, as that method can't count backwards. GPT-4, a system based on such a method, can count backwards, thus disproving your claim.
        woodruffw 3y ago
        I’m pretty sure the person you’re responding to is saying that counting is a formal operation with a formal definition, and that when GPT “counts” it isn’t doing that formal operation. The fact that it can semi-reliably emit ordered sequences isn’t counterevidence, for the same reason that memorizing the order of 0 to 100 isn’t evidence of an understanding of the inductive structure of counting.

by retube 3y ago

How does chat-gpt actually get this right? This would appear to require some degree of reasoning, but as far as i understand its output is purely probabilistic, based on existing corpus of text.

1270018080 3y ago

You answered your own question.

15457345234 3y ago
Can you elaborate?
- jw1224 3y ago
  Your question: > How does chat-gpt actually get this right? Your answer: > its output is purely probabilistic, based on existing corpus of text Because GPT was trained on existing text, some of which included numbers and counting, it's learnt the natural ordering of most common/everyday numbers. For larger or more complex numbers, it's learnt the patterns behind how they're constructed linguistically, which allows it to output a written count in sequence. This same pattern recognition doesn't work anywhere near as well for actual numerals (e.g. "47600", instead of "forty seven thousand six hundred"), as the tokenizer tends to break long numerals apart (e.g. into ["476", "00"]).
  - pbhjpbhj 3y ago
    In text, we don't often count in series, and it seems likely that we often choose a non-counting sequence: like 'I chose options 1, 2, 7' or 'my code was 0 1 2 5', whatever. Unless training included line-level skips, rather than just next-word skips (like word2vec) or concept-level associations? At the line level, or paragraph level, ordered numerical sequences are obviously very common in formal texts or in code. I've seen sentence based training, I suppose for code (which it seems GPT4 excells at) line-level training would be essential. Anyone recommend a mid-level read on this covering different modes of training and such; I'm happy with a bit of code and undergrad level maths. Thanks.
    - jw1224 3y ago
      > Unless training included line-level skips Yes, of course — GPT-4 was trained on all common character sequences, including linebreaks and other invisible characters. You can see how it works here: https://platform.openai.com/tokenizer https://platform.openai.com/tokenizer Nonetheless it doesn't need to have seen examples of line-level counting before. The "concept-level associations" you mentioned are an emergent property of the model, it forms its own concept-level associations as a result of being trained on such a massive dataset. It's what enables it to output original content which has never been seen before.
    - Zambyte 3y ago
      Maybe we count in series a lot more than you think. https://www.youtube.com/watch?v=WO2X3oZEJOA https://www.youtube.com/watch?v=WO2X3oZEJOA

pyinstallwoes 3y ago

Aren't your sentences probabilistically based on the existing corpus of text within your mind?

15457345234 3y ago
No? If that's how sentence forming worked it would be impossible to count backwards, or count at all for that matter.
- pyinstallwoes 3y ago
  Why? Numbers have directionality relative to the species.
- stavros 3y ago
  Me: Count backwards from ten GPT-4: 10, 9, 8, 7, 6, 5, 4, 3, 2, 1 Me: Count backwards from a million to 999990 GPT-4: 1,000,000, 999,999, 999,998, 999,997, 999,996, 999,995, 999,994, 999,993, 999,992, 999,991, 999,990 I don't know about that.
  - 15457345234 3y ago
    I don't know how that connects in any way to what I and the commenter preceding me wrote.
    - stavros 3y ago
      You said that sentences aren't formed by probability from an existing corpus, as that method can't count backwards. GPT-4, a system based on such a method, can count backwards, thus disproving your claim.
      - woodruffw 3y ago
        I’m pretty sure the person you’re responding to is saying that counting is a formal operation with a formal definition, and that when GPT “counts” it isn’t doing that formal operation. The fact that it can semi-reliably emit ordered sequences isn’t counterevidence, for the same reason that memorizing the order of 0 to 100 isn’t evidence of an understanding of the inductive structure of counting.