The Sign Estimator: LLM Alignment in the Face of Choice Heterogeneity

• URL: http://arxiv.org/abs/2510.23965v2
• Date: Wed, 29 Oct 2025 15:51:35 GMT
• Title: The Sign Estimator: LLM Alignment in the Face of Choice Heterogeneity
• Authors: Ali Aouad, Aymane El Gadarri, Vivek F. Farias,
• Abstract summary: Traditional alignment methods are vulnerable to heterogeneity in human preferences.<n>We propose a new method, dubbed the sign estimator, that provides a simple, provably consistent, and efficient estimator.
• Score: 4.957619545367733
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Traditional LLM alignment methods are vulnerable to heterogeneity in human preferences. Fitting a na\"ive probabilistic model to pairwise comparison data (say over prompt-completion pairs) yields an inconsistent estimate of the population-average utility -a canonical measure of social welfare. We propose a new method, dubbed the sign estimator, that provides a simple, provably consistent, and efficient estimator by replacing cross-entropy with binary classification loss in the aggregation step. This simple modification recovers consistent ordinal alignment under mild assumptions and achieves the first polynomial finite-sample error bounds in this setting. In realistic simulations of LLM alignment using digital twins, the sign estimator substantially reduces preference distortion over a panel of simulated personas, cutting (angular) estimation error by nearly 35% and decreasing disagreement with true population preferences from 12% to 8% compared to standard RLHF. Our method also compares favorably to panel data heuristics that explicitly model user heterogeneity and require tracking individual-level preference data-all while maintaining the implementation simplicity of existing LLM alignment pipelines.

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