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cs.LG2026
Patterning in Practice: Debiasing Reward Models with Susceptibilities
George Wang, Elizabeth Donoway, Daniel Murfet
Reward models trained on human preferences are known to suffer from length, formatting, and other stylistic biases. In this paper we use patterning, which reweights each preference…
cs.LG2026
Patterning: The Dual of Interpretability
George Wang, Daniel Murfet
Mechanistic interpretability aims to understand how neural networks generalize beyond their training data by reverse-engineering their internal structures. We introduce patterning…
cs.LG2026
Towards Spectroscopy: Susceptibility Clusters in Language Models
Andrew Gordon, Garrett Baker, George Wang +3
Spectroscopy infers the internal structure of physical systems by measuring their response to perturbations. We apply this principle to neural networks: perturbing the data distrib…