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.
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.
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.
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.
Okay, I see, you're expecting Jev to properly give 1/6 probability for each option. This is different from my intuition of how LLMs work, where their probabilities don't really work like this (I would expect LLM to also do something like 0.83 for 1).
That's right. It's normal behavior of LLMs. But what matters is TypeSafe argues it's different exactly on this point. The selling point of Jev is "calibrated probabilities", so I checked it on probability problems.
Jev and LLMs give other promises. Jev's RLCD training aims to make its probabilities calibrated such that given many cases where it assigns label Y about X% probability, Y should be the correct label about X% of the time.
> 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.
TypeSafe defines the probabilities Jev returns as "calibrated probabilities". "Probability" here means the probability of the answer being correct. If the probability is 10%, the choice should be correct about one time in ten. So, when Jev returns 83% probability it should be correct about 83 times out of 100, but the choices were only correct about 19 times out of 100, and the true probability is 1/6.
"Higher probability should correspond to a greater chance that the answer is correct."
As someone who hasn’t used Jev, this seems reasonable in the sense that “probability that I’ve given the best answer” seems like a useful thing to provide.
Anyway, if it is “probability that I gave the best answer” why 83%? I mean, the question is a sort of a textbook-style probability question in the sense that everything is well defined. Shouldn’t it be basically 100%?
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].
While I don't believe they are doing the following: you can calibrate by inspecting the reasoning traces. That is the relevant distribution. If you ask someone to explain how/why they are classifying something one way v another, you can get a reasonably good understanding of their confidence level.
This tells me the confidence of the LLM's belief about the response - which is different from the calibrated confidence score. The former also is useful (just not what I thought their advertisement sells - and from the article it seems like it tripped up others as well), and there are different techniques to extract such a value [1] [2], typically via "response sampling", i.e., interrogate the LLM slightly differently to see if it changes its answer.
I mean the model can learn from it during RL training. The confidence score is affected by the tokens prior to it it's output. I was using the word "you" loosely.
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.
The available API shapes have limited so much over the years. It's really hard to come up with a new one and have it used, so everyone just tries to fit their work into the existing APIs (chat/completions).
Good job to them for putting out something that does seem quite nice to use, and will likely get a bit of wider traction.
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.
I suspect they meant it as "they claim it is already calibrated, but it can't be, because there is no universally correct calibration", and not "it is not possible to calibrate Jev", but I read it as the latter at first too.
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.
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.
I had the opposite experience but I think it depends a lot on how you write the options. Vague labels gave me meh results. Once each option had an explicit "not for" list plus a couple of examples it got a lot better. That's for email sorting though, so pretty easy categories
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.
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.
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
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.
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.
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.
The value of grandparent comment is that it identifies an edge case to keep in mind and make well-defined -- keeps us from blindly trusting.
ask jev if god exists
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.
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?
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.
Okay, I see, you're expecting Jev to properly give 1/6 probability for each option. This is different from my intuition of how LLMs work, where their probabilities don't really work like this (I would expect LLM to also do something like 0.83 for 1).
That's right. It's normal behavior of LLMs. But what matters is TypeSafe argues it's different exactly on this point. The selling point of Jev is "calibrated probabilities", so I checked it on probability problems.
Jev and LLMs give other promises. Jev's RLCD training aims to make its probabilities calibrated such that given many cases where it assigns label Y about X% probability, Y should be the correct label about X% of the time.
One of the entire value props for Jev is that is exactly how it is supposed to work. That is one of the big claimed advantages over regular LLMs.
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."
I don't know if it's reasonable. What it isn't is calibrated.
Reminds me of this... https://xkcd.com/221/
So you want the probability that the answer is correct, but Jev is providing the probability that its answer is optimal?
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...
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).
Typically not! The distribution is not uniform.
https://www.youtube.com/watch?v=d6iQrh2TK98
> 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.
TypeSafe defines the probabilities Jev returns as "calibrated probabilities". "Probability" here means the probability of the answer being correct. If the probability is 10%, the choice should be correct about one time in ten. So, when Jev returns 83% probability it should be correct about 83 times out of 100, but the choices were only correct about 19 times out of 100, and the true probability is 1/6.
"Higher probability should correspond to a greater chance that the answer is correct."
https://docs.typesafe.ai/introduction/machine-learning-prime...
As someone who hasn’t used Jev, this seems reasonable in the sense that “probability that I’ve given the best answer” seems like a useful thing to provide.
Anyway, if it is “probability that I gave the best answer” why 83%? I mean, the question is a sort of a textbook-style probability question in the sense that everything is well defined. Shouldn’t it be basically 100%?
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...
While I don't believe they are doing the following: you can calibrate by inspecting the reasoning traces. That is the relevant distribution. If you ask someone to explain how/why they are classifying something one way v another, you can get a reasonably good understanding of their confidence level.
This tells me the confidence of the LLM's belief about the response - which is different from the calibrated confidence score. The former also is useful (just not what I thought their advertisement sells - and from the article it seems like it tripped up others as well), and there are different techniques to extract such a value [1] [2], typically via "response sampling", i.e., interrogate the LLM slightly differently to see if it changes its answer.
[1] Semantic Entropy https://www.nature.com/articles/s41586-024-07421-0
[2] Kernel Language Entropy https://openreview.net/pdf?id=j2wCrWmgMX
I mean the model can learn from it during RL training. The confidence score is affected by the tokens prior to it it's output. I was using the word "you" loosely.
It's on OpenRouter if you want to try it.
Thank you!
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.
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.
It’s not just price and speed, the API is very well designed for classification. Going beyond the current structured outputs.
The available API shapes have limited so much over the years. It's really hard to come up with a new one and have it used, so everyone just tries to fit their work into the existing APIs (chat/completions).
Good job to them for putting out something that does seem quite nice to use, and will likely get a bit of wider traction.
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.
I suspect they meant it as "they claim it is already calibrated, but it can't be, because there is no universally correct calibration", and not "it is not possible to calibrate Jev", but I read it as the latter at first too.
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
Ah, I see, you mean "Jev cannot possibly be calibrated for everyone as there is always missing context".
I understood it as "It is impossible to calibrate Jev".
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.
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.
Jev’s context should have signals/features to operate on. Same way LLMs can use CV & code to analyze an image.
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.
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
Welcome to AI hype.
do you have a benchmark name and results we can compare to other models?
I had the opposite experience but I think it depends a lot on how you write the options. Vague labels gave me meh results. Once each option had an explicit "not for" list plus a couple of examples it got a lot better. That's for email sorting though, so pretty easy categories
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).
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.
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.
If you want calibrated probabilities you'll be forced to fine-tune it
In future, we will see intiatives similar to OpenStreetMap for Textual data or Web similar to high quality non-contaminated steel.
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
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.
Is Jev good at detecting AI-generated text?
I asked Jev and it said 41% yes 59% no.
bro will soon find out it's a baysian classifier imported from sklearn lmao.