A Controlled Counterexample to Strong Proxy-Based Explanations of OOD Performance: in a Fixed Pretraining-and-Probing Setup
arXiv:2605.11554v2 Announce Type: replace Abstract: Task-agnostic structure proxies are often used to interpret why one pretraining corpus transfers better than another, but such explanations require the proxy to track the structure that matters for the downstream task. We test this requirement in a fixed pretraining-and-probing setup motivated by computationally bounded notions of learned structure, including epiplexity. The core question is whether a proxy ranking of two pretraining datasets must agree with their ranking by OOD probe accuracy. We show that it need not. First, we give a contr
This research is part of an ongoing academic effort to better understand the theoretical underpinnings and limitations of AI models. It addresses nuances in AI explainability, a persistent area of research.
For a strategic reader, this paper highlights the ongoing challenges in reliably interpreting AI model performance and the potential for misleading 'proxy-based explanations', indicating that current interpretability methods are still immature.
This paper does not change current practices but rather refines the academic understanding of AI interpretability, suggesting that simple metrics may not always align with true out-of-distribution performance.
- · AI interpretability researchers
- · Developers focused on robust OOD performance
- · Overly simplistic AI explanation frameworks
- · Users relying solely on proxy metrics for OOD performance evaluation
It directly suggests that existing methods for explaining OOD performance using structural proxies might be insufficient or misleading.
This could lead to a re-evaluation of interpretability metrics applied to AI models and encourage the development of more robust, task-specific evaluation techniques.
Longer term, it may contribute to a more nuanced approach to AI governance and regulation, where explainability requirements are understood with greater theoretical precision.
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