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cs.LG2026
From Constitutions to Control: Interpretable Rewards for Aligning Language Models
Johann D. Gaebler, Calvin Isley, Max Lamparth +2
Current approaches to aligning language models often make it hard to know what behavior is being rewarded or to change that reward in a targeted way. In particular, standard prefer…
cs.LG2026
Mitigating Label Bias with Interpretable Rubric Embeddings
Calvin Isley, Johann D. Gaebler, Sharad Goel
Statistical decision algorithms are increasingly deployed in domains where ground-truth labels are hard to obtain, such as hiring, university admissions, and content moderation. In…