How accurately calibrated is Jev?
An empirical evaluation of TypeSafe's Jev classifier shows its output probability distributions are poorly calibrated, heavily peaky, and prone to overconfidence.
read it Provides a thorough empirical evaluation of a new ML model's probability calibration with concrete experimental design and failure modes.
summary
An evaluation of TypeSafe's fast classifier model, Jev, across 1,000 settings involving physical and statistical distributions checks if its output probabilities match reality. Results show Jev is poorly calibrated, exhibiting extreme mode-seeking behavior and overconfidence even on simple uniform distributions. While Jev identifies correct distribution types, it breaks down on multi-step internal arithmetic and defaults to prompt cues rather than computing true probabilities.
key points
- Jev's output probability distributions scored a mean Total Variation distance of 0.518, barely better than a flat random guess.
- The model suffers from severe overconfidence, assigning high probability mass to peak bins while failing drastically on uniform distributions and coin/die simulations.
- Auxiliary tests show Jev correctly recognizes which statistical distribution applies, but its internal calculations break down on multi-step math and tracking powers of ten.
who should read it
Machine learning engineers and researchers interested in model evaluation, calibration, and classifier architectures.
discussion mixed
consensus
Commenters debate whether testing Jev on theoretical physics distributions is a fair benchmark, but generally agree its outputs function as rough reasonableness scores rather than true calibrated probabilities.
pushback
Some argue the test is mismatched to Jev's intended use-case of fast, practical data classification rather than functioning as a probability engine.
In the dice example[0] and in this one, the expected output is a distribution e.g. "1: 16.7%, 2: 16.7%...", not a random generation. I agree that in implementation, the architecture is not designed to accurately calculate or incorporate any known uncertainties like these, but the task itself is completely reasonable and arguably trivial.
IMO the lesson here is: even in trivial cases, Jev's outputs are just ~reasonableness scores which do not correspond to actual probabilities. They should not be treated as actual probabilities without careful calibration and plenty of meta-uncertainty about …
Notes that Jev's outputs are merely reasonableness scores rather than actual probabilities, warning against trusting them without rigorous calibration.
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