aiqbench analysis
This research demonstrates a counterintuitive finding where data imbalance enhances robust generalization in sufficiently capable transformers by preventing models from over-relying on spurious shortcuts. Through mechanistic analysis of gradient dynamics and circuit evolution, the study reveals that this effect is specific to multi-layer architectures, whereas simpler models remain trapped by imbalanced data. These insights are critical for advancing machine learning theory regarding how model capacity interacts with training distribution shifts to achieve adversarial robustness.
When Data Imbalance Helps: Robust Generalization Through Shortcut Saturation
arXiv:2607.10116v2 Announce Type: replace-cross Abstract: We study robust generalization under spurious correlations: tasks where a shortcut feature is correlated with the true label in training but anti-correlated in an adversarial held-out split. Varying the spurious ratio $r$ (the fraction of training examples where shortcut = true label) and model capacity, we find a counterintuitive result: data imbalance promotes generalization in sufficiently capable models. On a synthetic task where the true label is sum parity of an integer sequence and the shortcut is the parity of the maximum-valued element, a 2-layer, 2-head transformer generalized (reached $100\%$ adversarial accuracy) in 0% of seeds at $r{=}0.50$ but 77% of seeds at $r{=}0.90$. The effect is absent in 1-layer models, where imbalance instead traps the model on the shortcut. Through mechanistic analysis -- gradient conflict dynamics, circuit evolution, and QK/OV circuit ablations -- we characterize a mechanistic pathway consistent with imbalance promoting generalization.
Abstract:We study robust generalization under spurious correlations: tasks where a shortcut feature is correlated with the true label in training but anti-correlated in an adversarial held-out split. Varying the spurious ratio $r$ (the fraction of training examples where shortcut = true label) and model capacity, we find a counterintuitive result: data imbalance promotes generalization in sufficiently capable models. On a synthetic task where the true label is sum parity of an integer sequence and the shortcut is the parity of the maximum-valued element, a 2-layer, 2-head transformer generalized (reached $100\%$ adversarial accuracy) in 0% of seeds at $r{=}0.50$ but 77% of seeds at $r{=}0.90$. The effect is absent in 1-layer models, where imbalance instead traps the model on the shortcut. Through mechanistic analysis -- gradient conflict dynamics, circuit evolution, and QK/OV circuit ablations -- we characterize a mechanistic pathway consistent with imbalance promoting generalization.
Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
arXiv:2607.10116 [cs.LG]
(or arXiv:2607.10116v2 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2607.10116 Focus to learn more arXiv-issued DOI via DataCite
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