Build your own decision model
“System one” decision models are models that infer and respond with calibrated probabilities or every allowed answer.
Consider your everyday language model, to get typed output from it (JSON), you may use Structured Output to constrain the output to guaranteed valid JSON. While model prefills the input in one pass, it still has to go perform a pass for every token in order to generate a valid response.
In this example, 11 passes are required to generate the final output. (We’re not accounting for speculative decoding and other inference optimization techniques.)
Decision models such as Jev, make the assumption that there are fixed options we can select from and we can do so quickly by making a single pass. In this example we constrain the set of possible outputs to the options A, B, C, D, E. By masking other items in the vocabulary, the model can only emit those tokens. By selecting the highest probability output, we get our answer.
Since the outputs are constrained to only a fixed set of options, the model can’t select anything outside of those. This however does not guarantee that the output will be correct. It’s also common to treat the output token probabilities a confidence scores in this context, but without additional training, those scores likely reflect its confidence in what the next token will be rather than the true probability of the response being the correct answer.
We can emulate this behavior by constraining output tokens using an LLM. Here I’m using Qwen/Qwen3-1.7B
Running it with an simple question to test it yeilds the following output
The model was able to make sense of our input and make a prediction that reasonably corresponds to the correct answer.
We can test the accuracy of the model by running it against public datasets. I ran this against a random sample holdout of CommonsenseQA
Not bad for a 1.7B model, Running a quick finetune on the dataset gives us slightly better performance
Testing the model against a very ambiguous problem demonstrates an interesting problem.
There should be no clear answer here, but treating the output probabilities as a pseudo “confidence” score, shows that the model is extremely overconfident in this answer.
If we bin the confidence score ranges in the eval I ran earlier, we can see that the model’s confidence does not match its accuracy. This means that the model is not calibrated.
We can notice that the model tends to be extremely overconfident in the 0.9 - 1.0 bin but it’s only correct 70% of the time. When it makes a prediction with 0.8 - 0.9 confidence it’s only accurate ~40% of the time. This means that the model is generally overconfident in its predictions.
Since our goal is to have the model output scores that is reflective of its accuracy, one method we can use to callibrate it is through temperature scaling. By modifying the temperature value, we can flatten its output probability distribution curve and scale it to approximate its accuracy.
Curve fitting fit the temperature parameter to the model’s accuracy, I found 3.797280788421631 as a temp value.
This gets us a much better calibration. If you want to play around with this, I made a GitHub repo with scripts that walk you through building a dataset, evaluating, finetuning and calibrating your own model. I encourage pulling it and trying it on other bigger models.