Cheap, Consistent, Calibrated, but Biased? Typed Decision Models as Judges

Title:Cheap, Consistent, Calibrated, but Biased? Typed Decision Models as Judges

Abstract:Typed decision models (TDMs) return a probability for each allowed answer instead of generating text, which makes them candidates for cheaply replacing LLM judges that compare two answers. Whether they share the biases of LLM judges is untested. We run the CALM benchmark's bias tests on eight TDMs and seven LLM judges of 2024 and 2026. Every TDM is biased, in three to eight of twelve tests. Emotional tone affects them most, and rewriting the better answer in such a tone lowers the accuracy of a typical TDM from 99% to 42%. TDMs are more biased than 2026 LLM judges. All eight change verdicts when the answers swap places, which the 2026 LLM judges hardly do, and several side with an answer that most people allegedly prefer or that a famous model allegedly wrote. Yet both kinds of judge are tripped up by the same prompts. The TDMs' probabilities show that most tests mainly flip verdicts the TDM was unsure of, and that good calibration does not protect against bias. As TDMs are more consistent and 37 times cheaper than 2026 LLM judges, our results suggest they can be attractive judges if their biases are tested for the task.

Computation and Language (cs.CL)

arXiv:2610.18122 [cs.CL]

(or arXiv:2610.18122v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2610.18122 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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