Jev Can't Be Calibrated

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Jev Can't Be Calibrated

(alexmolas.com)

by alexmolas

kantahayashi
312d
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...
1. alexmolas • OP
  22d
  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.
  1. kantahayashi
    12d
    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.
    [deleted]
    02d
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2. throwaway_7274
  02d
  If you instead offer probabilities as answers, it picks the right one with high credence.
3. drtz
  22d
  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.
  1. tomrod
    02d
4. edot
  02d
  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.
5. tomrod
  02d
  Echoes a bit of a philosophical distinction with a long history: "Knightian Uncertainty" versus "Probability".
6. seizethecheese
  122d
  Maybe I’m confused here, but it’s perfectly reasonable to just guess the same dice roll every time right?
  1. kantahayashi
    92d
    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.
7. lesam
  12d
  So you want the probability that the answer is correct, but Jev is providing the probability that its answer is optimal?
  1. kantahayashi
    02d
    Yes, and TypeSafe itself says Jev returns "calibrated probabilities", which is the former.
    From TypeSafe docs:
    "Higher probability should correspond to a greater chance that the answer is correct."
    "Outcomes assigned a probability of 0.2 should occur about 20% of the time."
    https://docs.typesafe.ai/introduction/machine-learning-prime...
8. [deleted]
  02d
  [deleted]
9. scotty79
  22d
  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.
  1. formerly_proven
    12d
    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).
10. esperent
  22d
  > 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.
  1. kantahayashi
abhgh
52d
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 they 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...
1. danielmarkbruce
jackb4040
02d
This is why I don't understand why everyone's freaking out about it. By far the biggest problem with LLM classifiers is that they treat every individual business as the blurry average of all businesses in their training data. Being lighter is fine if you control for everything else, but at least at my company we would actually have room for a significantly more expensive / slower classifier if it were demonstrably better at following instructions.
croemer
22d
Article says that it _can_ be calibrated, it just isn't out of the box. So the title seems contradicted by the body.
> If you want calibrated probabilities you’ll still need to recalibrate Jev’s probabilities on your own data. The good news is that is cheap. A few hundred labeled examples from your actual data can be enough to fit a Platt scaling on top of Jev’s scores.
1. alexmolas • OP
  02d
  Yes, good point. I meant that the Jev can't be calibrated for everyone out of the box. I find it important because this is one of their main claims. They literally say
  > Outcomes assigned a probability of 0.2 should occur about 20% of the time.
  and this is not true
bnbn88
32d
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.
1. jrochkind1
  02d
  They seem to have a really good social media astroturf marketing campaign.
time0ut
02d
I have been running a series of experiments on Jev since its release targeted at understanding it, seeing how it handles real use cases I have, and maybe figure out what it is inside.
Some of my tests do point towards what this post says. I was not successful in getting it's score to align with an existing rubric I had. It 'worked' but it was off and compressed from where I wanted it to be. Not a bad starting point, but I couldn't get it to move to where I intended the rubric to be. It wasn't the most robust test and I didn't spend a lot of time trying, but it wasn't just instantly magical.
However, it does seem genuinely useful just by being fast and cheap and good enough, so I am still a bit hyped.
daft_pink
12d
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.
1. oreoftw
  02d
  Jev’s context should have signals/features to operate on. Same way LLMs can use CV & code to analyze an image.
sreekanth850
02d
People on X will use JEV for self driving cars too. That is the height of stupidity. I’ve seen this in so many tweets.
empath75
42d
I spent a couple of days with it and it is fast and cheap but not especially good at classifying.
1. alexmolas • OP
  12d
  "Not especially good at classifying" does sound like a serious drawback for a classifier
apwheele
02d
While this is true, it is still possible to take the probabilities and make determinations of recall and false positives using conformal sets, https://crimede-coder.com/blogposts/2026/ConfClassification
(Or just use a model to re-calibrate the probabilities, I like the conformal approach though as those rates are what I often care about.)
You just need some labelled data to generate the "corrected" probabilities (or thresholds to meet the specified error rates).
simonw
22d
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.
1. edot
  02d
  But why? Jev-style models seem useful for "I have no clue what my incoming distribution looks like but I need to give some sort of answer". If I know what my incoming distribution looks like I'll just upload a CSV of that into ChatGPT and ask it to fit a basic ML model on my data.
conmod278
02d
In future, we will see intiatives similar to OpenStreetMap for Textual data or Web similar to high quality non-contaminated steel.
a_c
32d
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
1. hbrn
  02d
  I suspect that it's as handy as a coin flip.
  And if I'm being charitable to Jev (which is nearly impossible at this point), detecting whether blob of text is AI generated is not a "system one" question.
maxdose
01d
bro will soon find out it's a baysian classifier imported from sklearn lmao.