Small-to-Large Generalization: Training Data Influences Models Consistently Across Scale

Alaa Khaddaj, Logan Engstrom, Aleksander Madry

Keywords: data attribution

Abstract:

Choice of training data distribution greatly influences model behavior. Yet, in
large-scale settings, precisely characterizing *how* changes in training
data affects predictions is often difficult due to model training costs. Current
practice is to instead extrapolate from scaled down, inexpensive-to-train proxy
models. However, changes in data do not influence smaller and larger models
identically. Therefore, understanding how choice of data affects large-scale
models raises the question: how does training data distribution influence model
behavior across compute scale? We find that small- and large-scale language
model predictions (generally) *do* highly correlate across choice of
training data. Equipped with these findings, we characterize how proxy scale
affects effectiveness in two downstream proxy model applications: data
attribution and dataset selection.

Primary Area: other topics in machine learning (i.e., none of the above)

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Submission Number: 10866