4 ms·

They can't possibly remember even a few positions. Don't you know the legend about rice grains on a chess board? The claim here is not about intelligence, it i

by lostmsu 10d ago

They can't possibly remember even a few positions. Don't you know the legend about rice grains on a chess board? The claim here is not about intelligence, it is about generality. There's no doubt for me the LLMs are intelligent.

HarHarVeryFunny 10d ago
> They can't possibly remember even a few positions. Sure they could, but that's irrelevant. A chess position is just a matter of remembering what piece number is on each square - just a list of 64 numbers. A trained model may store a trillion numbers (weights). It could store a TON of chess positions if it needed to. However, that's not how LLMs work. They don't memorize inputs - they predict them, based on discovering predictive patterns, and those predictive patterns are not input patterns (e.g. board positions). They are deep patterns (maybe 100 layers of abstraction removed from the input), representing partial inputs, generalized across many training samples. > Don't you know the legend about rice grains on a chess board? Sure, but this has nothing to do with chess, and nothing to do with how many games were in the LLM's training data. > The claim here is not about intelligence, it is about generality. There's no doubt for me the LLMs are intelligent. Intelligent humans created the training data, and the LLM attempts to predict (copy) the training data, so of course it looks intelligent. If I say "E=mc^2", does that make you think I am Einstein?
- lostmsu 10d ago
  > prediction which is closer to memorization > don't memorize inputs - they predict them I feel some tension here. > rice grains on a chess board? Sure, but this has nothing to do with chess, and nothing to do with how many games were in the LLM's training data. > just a list of 64 numbers > remember even a few positions? Sure they could, but that's irrelevant. I don't think you do. Or rather you do know the legend but for some funny reason seem to be unable to apply its lesson here, because you are talking about enormous terabytes of training data. > Intelligent humans created the training data, and the LLM attempts to predict (copy) the training data, so of course it looks intelligent. If for you it is about intelligence, I am out of this discussion.
  - HarHarVeryFunny 10d ago
    You are talking about 2^64 being a huge number I assume ? If not, then what are you talking about ? If yes, then what is the relevance to an LLM playing chess ?
    - lostmsu 9d ago
      > just a list of 64 numbers > remember even a few positions? Sure they could A rough estimate of number of positions across all X move games is X^10. For 15 moves it is hopeless to remember even a relatively small part of them. Typical game has 40 turns, 1 move per player, so 80 moves.
      - HarHarVeryFunny 9d ago
        1) The number of unique chess games that could theoretically be played (but mostly never have been), is irrelevant to what an LLM is remembering. It can only remember what was in it's training data - a far smaller number of maybe 10's of millions of games (of 30-50 moves each). 2) An LLM is not going to memorize vs generalize when there is no training pressure to do so. You might expect it to memorize book openings that occur over and over in the training data, but not some random non-celebrity game that occurs once in the Lichess dataset and is never again referred to. > They can't possibly remember even a few positions. Don't you know the legend about rice grains on a chess board? If the wise man was a bit wiser, he'd have asked for his rice on a snakes & ladders board (100 squares, not 64) and would have had 2^36 more rice, which is equally irrelevant.

by lostmsu 10d ago

HarHarVeryFunny 10d ago

> They can't possibly remember even a few positions. Sure they could, but that's irrelevant. A chess position is just a matter of remembering what piece number is on each square - just a list of 64 numbers. A trained model may store a trillion numbers (weights). It could store a TON of chess positions if it needed to. However, that's not how LLMs work. They don't memorize inputs - they predict them, based on discovering predictive patterns, and those predictive patterns are not input patterns (e.g. board positions). They are deep patterns (maybe 100 layers of abstraction removed from the input), representing partial inputs, generalized across many training samples. > Don't you know the legend about rice grains on a chess board? Sure, but this has nothing to do with chess, and nothing to do with how many games were in the LLM's training data. > The claim here is not about intelligence, it is about generality. There's no doubt for me the LLMs are intelligent. Intelligent humans created the training data, and the LLM attempts to predict (copy) the training data, so of course it looks intelligent. If I say "E=mc^2", does that make you think I am Einstein?

lostmsu 10d ago
> prediction which is closer to memorization > don't memorize inputs - they predict them I feel some tension here. > rice grains on a chess board? Sure, but this has nothing to do with chess, and nothing to do with how many games were in the LLM's training data. > just a list of 64 numbers > remember even a few positions? Sure they could, but that's irrelevant. I don't think you do. Or rather you do know the legend but for some funny reason seem to be unable to apply its lesson here, because you are talking about enormous terabytes of training data. > Intelligent humans created the training data, and the LLM attempts to predict (copy) the training data, so of course it looks intelligent. If for you it is about intelligence, I am out of this discussion.
- HarHarVeryFunny 10d ago
  You are talking about 2^64 being a huge number I assume ? If not, then what are you talking about ? If yes, then what is the relevance to an LLM playing chess ?
  - lostmsu 9d ago
    > just a list of 64 numbers > remember even a few positions? Sure they could A rough estimate of number of positions across all X move games is X^10. For 15 moves it is hopeless to remember even a relatively small part of them. Typical game has 40 turns, 1 move per player, so 80 moves.
    - HarHarVeryFunny 9d ago
      1) The number of unique chess games that could theoretically be played (but mostly never have been), is irrelevant to what an LLM is remembering. It can only remember what was in it's training data - a far smaller number of maybe 10's of millions of games (of 30-50 moves each). 2) An LLM is not going to memorize vs generalize when there is no training pressure to do so. You might expect it to memorize book openings that occur over and over in the training data, but not some random non-celebrity game that occurs once in the Lichess dataset and is never again referred to. > They can't possibly remember even a few positions. Don't you know the legend about rice grains on a chess board? If the wise man was a bit wiser, he'd have asked for his rice on a snakes & ladders board (100 squares, not 64) and would have had 2^36 more rice, which is equally irrelevant.