Earlier quoted context omitted.
> It understands you very well No, it creates output that intuitively feels like like it understands you very well, until you press it in ways that pop the illusion. To truly conclude it understands things, one needs to show some internal cause and effect, to disprove a Chinese Room scenario. https://en.wikipedia.org/wiki/Chinese_room
> No, it creates output that intuitively feels like like it understands you very well, until you press it in ways that pop the illusion. I would say even a foundation model, without supervised instruction tuning, and without RLHF, understands text quite well. It just predicts the most likely continuation of the prompt, but to do so effectively, it arguably has to understand what the text means.
But it messes something so simple up because it doesn't actually understand things. It's just doing math, and the math has holes and limitations in how it works that causes simple errors like this.
If it was truly understanding, then it should be able to understand and figure out how to work around these such limitations in the math.
At least in my opinion.