It really doesn’t. You’re just describing the “fancy” part of “fancy autocomplete.” No one was ever really suggesting that they only predict the next word. If that was the case they would just be autocomplete, nothing fancy about it.
What’s being conveyed by “fancy autocomplete” is that these models ultimately operate by combining the most statistically likely elements of their dataset, with some application of random noise. More noise creates more “creative” (meaning more random, less probable) outputs. They do not actually “think” as we understand thought. This can clearly be seen in the examples given in the article, especially to do with math. The model is throwing together elements that are statistically proximate to the prompt. It’s not actually applying a structured, logical method the way humans can be taught to.
Genuine question regarding the rhyme thing, it can be argued that “predicting backwards isn’t very different” but you can’t attribute generating the rhyme first to noise, right? So how does it “know” (for lack of a better word) to generate the rhyme first?
It already knows which words are, statistically, more commonly rhymed with each other. From the massive list of training poems. This is what the massive data sets are for. One of the interesting things is that it’s not predicting backwards, exactly. It’s actually mathematically converging on the response text to the prompt, all the words at the same time.
It really doesn’t. You’re just describing the “fancy” part of “fancy autocomplete.” No one was ever really suggesting that they only predict the next word. If that was the case they would just be autocomplete, nothing fancy about it.
What’s being conveyed by “fancy autocomplete” is that these models ultimately operate by combining the most statistically likely elements of their dataset, with some application of random noise. More noise creates more “creative” (meaning more random, less probable) outputs. They do not actually “think” as we understand thought. This can clearly be seen in the examples given in the article, especially to do with math. The model is throwing together elements that are statistically proximate to the prompt. It’s not actually applying a structured, logical method the way humans can be taught to.
Unfortunately, these articles are often written by people who don’t know enough to realize they’re missing important nuances.
Genuine question regarding the rhyme thing, it can be argued that “predicting backwards isn’t very different” but you can’t attribute generating the rhyme first to noise, right? So how does it “know” (for lack of a better word) to generate the rhyme first?
It already knows which words are, statistically, more commonly rhymed with each other. From the massive list of training poems. This is what the massive data sets are for. One of the interesting things is that it’s not predicting backwards, exactly. It’s actually mathematically converging on the response text to the prompt, all the words at the same time.