[2509.01938] EigenBench: A Comparative Behavioral Measure of Value Alignment

Computer Science > Artificial Intelligence arXiv:2509.01938 (cs) [Submitted on 2 Sep 2025 (v1), last revised 1 Mar 2026 (this version, v4)] Title:EigenBench: A Comparative Behavioral Measure of Value Alignment Authors:Jonathn Chang, Leonhard Piff, Suvadip Sana, Jasmine X. Li, Lionel Levine View a PDF of the paper titled EigenBench: A Comparative Behavioral Measure of Value Alignment, by Jonathn Chang and 4 other authors View PDF Abstract:Aligning AI with human values is a pressing unsolved problem. To address the lack of quantitative metrics for value alignment, we propose EigenBench: a black-box method for comparatively benchmarking language models' values. Given an ensemble of models, a constitution describing a value system, and a dataset of scenarios, our method returns a vector of scores quantifying each model's alignment to the given constitution. To produce these scores, each model judges the outputs of other models across many scenarios, and these judgments are aggregated with EigenTrust (Kamvar et al., 2003), yielding scores that reflect a weighted consensus judgment of the whole ensemble. EigenBench uses no ground truth labels, as it is designed to quantify subjective traits for which reasonable judges may disagree on the correct label. Hence, to validate our method, we collect human judgments on the same ensemble of models and show that EigenBench's judgments align closely with those of human evaluators. We further demonstrate that EigenBench can recover model r...