Jun 2, 2025

Approximating Human Preferences Using a Multi-Judge Learned System

Eitan Sprejer, Fernando Avalos, José Pedro Brito de Azevedo Faustino, Augusto Mariano Bernardi

Details

Details

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In this work, we introduced a learned approach to aggregating multi-judge scores: using a GAM and a simple MLP as an alternative to traditional, non-learned methods like averaging. Our models outperform the naive baseline in predicting simulated human preferences, demonstrating that learned aggregation can better capture complex evaluative signals.

Cite this work:

@misc {

title={

@misc {

},

author={

Eitan Sprejer, Fernando Avalos, José Pedro Brito de Azevedo Faustino, Augusto Mariano Bernardi

},

date={

6/2/25

},

organization={Apart Research},

note={Research submission to the research sprint hosted by Apart.},

howpublished={https://apartresearch.com}

}

Reviewer's Comments

Reviewer's Comments

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This work was done during one weekend by research workshop participants and does not represent the work of Apart Research.
This work was done during one weekend by research workshop participants and does not represent the work of Apart Research.