If you instead offer probabilities as answers, it picks the right one with high credence.

In the early Gemini 2 days (don't remember which version exactly) I had Gemini running as a voice assistant in my kitchen, and asked it to flip a coin and tell me if it was heads or tails. It responded with "heads". I was curious if it was actually doing something to simulate randomness, so I asked a few more times and saw a pattern: "tails", "heads", "tails", "heads"...

It continued alternating between the two until I got bored (around a dozen turns).

Unless your specific test is baked into its training, real probabilities require math and rough approximation at a minimum needs reasoning to sanity-check. Jev does neither. This isn't a new problem or anything unique to Jev.

Hah! I did the exact same tests as you! I found that if you give it the choice to say "not sure", it picks that 100% of the time. But if you pin it in a corner, then yes it does these weird things. Also yes, the continuous options were much more accurate than the choices. Not sure why that is.

But "problems about actual probabilities whose answers can't be known at all" are exactly the problems where calibration is important. Since calibration is one of the big claims about Jev I'd expect it to perform well in these problems.

Echoes a bit of a philosophical distinction with a long history: "Knightian Uncertainty" versus "Probability".

Maybe I’m confused here, but it’s perfectly reasonable to just guess the same dice roll every time right?

So you want the probability that the answer is correct, but Jev is providing the probability that its answer is optimal?

Did you expect it to be good at it?

Humans also don't give a perfect 1/n probability when asked for a random number.

> The true probability of face 1 is 1/6, but Jev always chose face 1 and the probability it returned was about 83%

I think this is a misunderstanding of what that 83% probability means. You think you're asking Jev to assign a probability to a dice roll, and if so then obviously it should pick each answer 1/6 of the time.

However, what Jev is doing is assigning a probability to the answer it should choose. And it's choosing 1 with 83% probability, as the correct answer to give, when there's no additional knowledge available.

1 is a perfectly valid answer here. In the absence of other data there's no more (nor less) valid answer than that. and I assume that just like LLMs, the token prediction must assign higher probability to one outcome.

If you keep feeding the previous answers back in (if you can do that with Jev) I assume it would start giving other numbers because it's no longer operating from zero info. Maybe that would even reach ~1/6 after enough rounds, although maybe not.