Exploring the Design of Adaptation Protocols for Improved Generalization and Machine Learning Safety

TL;DR

This paper investigates how different adaptation protocols for large-scale pretrained models affect out-of-distribution generalization and safety metrics, revealing trade-offs and proposing strategies to mitigate them through data augmentation.

Contribution

It systematically evaluates adaptation protocols across various distribution shifts and safety metrics, highlighting their trade-offs and proposing augmentation-based methods for improvement.

Findings

Protocols induce different trade-offs in generalization and safety.

Pairing data augmentation with protocols can reduce trade-offs.

Hardness-promoting augmentations during adaptation improve robustness.

Abstract

While directly fine-tuning (FT) large-scale, pretrained models on task-specific data is well-known to induce strong in-distribution task performance, recent works have demonstrated that different adaptation protocols, such as linear probing (LP) prior to FT, can improve out-of-distribution generalization. However, the design space of such adaptation protocols remains under-explored and the evaluation of such protocols has primarily focused on distribution shifts. Therefore, in this work, we evaluate common adaptation protocols across distributions shifts and machine learning safety metrics (e.g., anomaly detection, calibration, robustness to corruptions). We find that protocols induce disparate trade-offs that were not apparent from prior evaluation. Further, we demonstrate that appropriate pairing of data augmentation and protocol can substantially mitigate this trade-off. Finally, we hypothesize and empirically see that using hardness-promoting augmentations during LP and then FT with augmentations may be particularly effective for trade-off mitigation.