- 20comments
- 40comments
- 11comments
- 49comments
- 76comments
- 4comments
- 141comments
- 189comments
- 24comments
- 800comments
- 12comments
- 111comments
- 76comments
- 1031comments
- —discuss
- 38comments
- 3comments
- 415comments
- 271comments
- 67comments
- 232comments
- 577comments
- 175comments
- 64comments
- 182comments
- 535comments
- 160comments
- 2comments
- 32comments
- 117comments
I tested Jev with a fair die 400 times without telling it the die result. The true probability of face 1 is 1/6, but Jev always chose face 1 and the probability it returned was about 83%. I also tested with a fair coin 200 times and got 0.92 probability.
I did several tests and I think Jev is good at problems with a correct answer but weak at problems about actual probabilities whose answers can't be known at all.
Write-up: "Jev Does Not Play Dice" https://kantahayashiai.github.io/posts/jev-does-not-play-dic...
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.
I agree. I think it's odd behavior too. Jev should be good at actual probability problems given the phrase "calibrated probabilities" TypeSafe uses for Jev. Maybe the reason is the data used in their training method (RLCD). If all the data consists of problems with a correct answer, I think this kind of odd behavior could happen.
Echoes a bit of a philosophical distinction with a long history: "Knightian Uncertainty" versus "Probability".
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.
If you instead offer probabilities as answers, it picks the right one with high credence.
Maybe I’m confused here, but it’s perfectly reasonable to just guess the same dice roll every time right?
I don't know if it's reasonable. What it isn't is calibrated.
Yes. There's no problem with choosing the same face every time. The problem is the probability it attached to the choice. Jev gave face 1 an 83% probability while the true probability is 1/6.
Do you provide Jev that the probability is 1/6 and yet it gives back a probability that is way off?
Yes. For example, one of the prompts said "The die is unbiased: each of the six faces has probability exactly 1/6."
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.
Humans give way more random answers than LLMs to questions like "give me a random number between 1-100" (when not giving the LLM any tool calls).
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.
In future, we will see intiatives similar to OpenStreetMap for Textual data or Web similar to high quality non-contaminated steel.
I'm really looking for a multi-modal image capable version of Jev.
If we could get machine learning type results on images without training, that would be fantastic.
Fine tuning LLMs has turned out to be mostly not worth the effort, but I wonder if fine tuning Jev-style models will turn out to be a whole lot more useful.
If you want calibrated probabilities you'll be forced to fine-tune it
I’m using Jev to classify a blob of text I see in browser with an extension. Calibrated? No. But it’s handy enough. If a large blob of text is likely AI generated, I’m very likely to skip it
Is Jev good at detecting AI-generated text?
I asked Jev and it said 41% yes 59% no.
I spent a couple of days with it and it is fast and cheap but not especially good at classifying.
"Not especially good at classifying" does sound like a serious drawback for a classifier
I like this post. I haven't had time to dig into Jev (they aren't accepting new signups), but calibrated probabilities is one of their pitches that caught my attention. And I was wondering how does one offer them on user data. Standard calibration essentially ensures that if a score of 0.8 accompanies a positive prediction (assuming the simple case of binary classification), then if you gathered together all predictions with a score of 0.8, around 80% will be correct.
If you have just one example you're sending to a model, how would you guarantee 80% over your data?
FYI, for an overview, scikit's page on calibration is great [1], and my answer on Quora from a long time ago covers a specific type [2].
[1] https://scikit-learn.org/stable/modules/calibration.html
[2] https://www.quora.com/How-is-isotonic-regression-used-in-pra...
This hype is caused by the price and the speed since most people don't know about small fast models and use big models for everything.
They seem to have a really good social media astroturf marketing campaign.