Predicting the Performance of Foundation Models via Agreement-on-the-Line
<|reference_start|>Predicting the Performance of Foundation Models via Agreement-on-the-Line: Estimating the out-of-distribution performance in regimes where labels are scarce is critical to safely deploy foundation models. Recently, it was shown that ensembles of neural networks observe the phenomena ``agreement-on-the-line'', which can be leveraged to reliably predict OOD performance without labels. However, in contrast to classical neural networks that are trained on in-distribution data from scratch for numerous epochs, foundation models undergo minimal finetuning from heavily pretrained weights, which may reduce the ensemble diversity needed to observe agreement-on-the-line. In our work, we demonstrate that when lightly finetuning multiple runs from a $\textit{single}$ foundation model, the choice of randomness during training (linear head initialization, data ordering, and data subsetting) can lead to drastically different levels of agreement-on-the-line in the resulting ensemble. Surprisingly, only random head initialization is able to reliably induce agreement-on-the-line in finetuned foundation models across vision and language benchmarks. Second, we demonstrate that ensembles of $\textit{multiple}$ foundation models pretrained on different datasets but finetuned on the same task can also show agreement-on-the-line. In total, by careful construction of a diverse ensemble, we can utilize agreement-on-the-line-based methods to predict the OOD performance of foundation models with high precision.<|reference_end|>
@article{saxena2024predicting,
    title={Predicting the Performance of Foundation Models via
  Agreement-on-the-Line},
    author={Rahul Saxena, Taeyoun Kim, Aman Mehra, Christina Baek, Zico Kolter,
  Aditi Raghunathan},
    journal={arXiv preprint arXiv:2404.01542},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.01542},
    primaryClass={cs.LG}
}