Why language models hallucinate

> Think about it like a multiple-choice test. If you do not know the answer but take a wild guess, you might get lucky and be right. Leaving it blank guarantees a zero. In the same way, when models are graded only on accuracy, the percentage of questions they get exactly right, they are encouraged to guess rather than say “I don’t know.”

To me, this seems to be an "US-American" way of thinking about multiple-choice tests. Other common ways to grade multiple-choice test that I have seen commonly are:

1. If the testee has the information that exactly one of N given choices is correct:

1.1 Give N-1 points for the correct answer, and -1 [negative one] point(s) for a wrong answer. This way, if the testee just answers the questions randomly, he will as expected value score 0 points.

1.2 A more brutal way if N>=3: the correct answer gives 1 point, all wrong answers give -1 points. You should learn your lesson only to give an answer if it is [alliteration unintended :-) ] correct (if N=2, the grading is identical to 1.1).

2. If there are possibly multiple correct answers, turn each item into choices of "yes" or "no" (with the option to give no answer). The correct choice gives you 1 point, the wrong gives you -1 point (i.e. as in 1.1).