It’s Luna, their small, low-intelligence model. But still impressive to see.

I also found this screenshot on Reddit: The model spent 26 seconds for some reason 🤔

  • BeefAndPoultry@lemmus.orgEnglish
    6·
    12 days ago

    This can happen because of tokenization. Tell it to execute code to do the letter counting and both models will succeed every time

    • hoshikarakitaridia@lemmy.worldEnglish
      4·
      12 days ago

      Kind of but not really. AIs learn to predict text but they can’t really do math the traditional way. They learn the tokens, which are more than syllables and less than a word long. So because they don’t do math, LLMs in particular speculate “something like 3 comes after this question” because in training it has seen similar questions answered with “3”. That’s the jist of it.

    • ffhein@lemmy.worldEnglish
      4·
      12 days ago

      According to this page ChatGPT would tokenize “seventeen” as [“se”, “venteen”] but AFAIK tokens become multi-dimensional vectors which represents its meaning (all vectors are the same length and the numbers in the vector have nothing to do with the letters that spell it out, just so you don’t imagine something like a C++ string). So an LLM can’t answer how many of each letters there is in a token either, unless explicitly trained to do so. To be able to immediately answer this, I think they would need to include huge amounts of training data to cover all tokens and letter combinations.

      But I would have expected that since the strawberry incident, all models would have been trained to do what Qwen tried to do here, and break the word down into letters. Because “s e v e n t e e n” tokenizes to [“s”, “e”, “v”, “e”, “n”, “t”, “e”, “e”, “n”] so then they only have to train the model to be able to convert any token into its individual letter, and to count occurrences of something in an array.

      However, if you disallow thinking and force the LLM to answer immediately, then you’re back to the original problem and will likely get an incorrect answer.