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Predicting the Out-of-Distribution Performance of Foundation Models Using Agreement-on-the-Line


Core Concepts
Estimating the out-of-distribution (OOD) performance of foundation models is critical for their safe deployment, but acquiring OOD labels is often costly. The authors demonstrate that by carefully constructing diverse ensembles of finetuned foundation models, the agreement-on-the-line (AGL) phenomenon can be leveraged to reliably predict OOD performance without labels.
Abstract
The authors investigate the ability to predict the out-of-distribution (OOD) performance of foundation models using the agreement-on-the-line (AGL) phenomenon. Key highlights: For a single base foundation model, the authors find that only randomly initializing the linear head can reliably induce AGL in the resulting ensemble, while data ordering and subsetting do not. This contrasts with neural networks trained from scratch, where AGL is more robust to the source of diversity. The authors show that ensembles of foundation models finetuned from different base models (e.g., GPT, OPT, Llama) can also exhibit AGL, even when the base models have different levels of effective robustness. By constructing diverse ensembles using random head initialization and multiple base models, the authors demonstrate that AGL-based methods can accurately predict the OOD performance of foundation models, outperforming other baselines like confidence-based methods. The authors conclude that carefully tuning ensemble diversity is crucial for leveraging AGL to estimate the OOD performance of foundation models, which have different training dynamics compared to neural networks trained from scratch.
Stats
Across vision benchmarks like CIFAR10C, CIFAR100C, and ImageNetC, linear probed CLIP models exhibit a strong linear correlation between in-distribution (ID) and out-of-distribution (OOD) accuracy. On the SQuAD-Shifts and MNLI-Mismatched to SNLI text classification and question-answering benchmarks, full finetuned GPT2, OPT, and Llama models also show a strong linear correlation between ID and OOD performance.
Quotes
"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 multiple foundation models pretrained on different datasets but finetuned on the same task can also show agreement-on-the-line."

Deeper Inquiries

How do the findings on AGL in foundation models extend to other types of distribution shifts, such as long-tailed distributions or adversarial perturbations

The findings on Agreement-on-the-Line (AGL) in foundation models can be extended to other types of distribution shifts, such as long-tailed distributions or adversarial perturbations, by considering the impact of ensemble diversity on model performance. In the context of long-tailed distributions, where the frequency of different classes varies significantly, diverse ensembles of finetuned foundation models can help mitigate the challenges posed by imbalanced data. By incorporating randomness in the training process, such as random head initialization or data subsetting, the models in the ensemble can capture a broader range of scenarios present in long-tailed distributions. This diversity allows the models to learn more robust and generalizable representations that can better handle the challenges posed by imbalanced data. Similarly, when it comes to adversarial perturbations, diverse ensembles of foundation models can enhance robustness against these perturbations. Adversarial examples are crafted to deceive neural networks, leading to misclassification or incorrect predictions. By leveraging AGL and ensuring ensemble diversity through different sources of randomness during training, foundation models can learn to make more consistent predictions in the presence of adversarial perturbations. This diversity helps the models to generalize better and adapt to unseen perturbations, ultimately improving their robustness in the face of adversarial attacks.

What are the implications of the authors' findings on the generalization behavior of foundation models compared to neural networks trained from scratch

The implications of the authors' findings on the generalization behavior of foundation models compared to neural networks trained from scratch are significant in understanding the role of pretraining and finetuning in model performance. The study highlights that while traditional neural networks trained from scratch may exhibit Agreement-on-the-Line (AGL) phenomena with diverse ensembles, foundation models undergo minimal finetuning from heavily pretrained weights, which can impact ensemble diversity and the observation of AGL. The findings suggest that for foundation models, the choice of randomness during training, particularly in the initialization of the linear head, plays a crucial role in inducing AGL and improving OOD performance estimation. This indicates that even with minimal finetuning, foundation models can achieve robustness and generalization comparable to neural networks trained from scratch, provided that the ensemble is carefully constructed to incorporate diversity. Overall, the insights shed light on the unique behavior of foundation models and their ability to leverage ensemble diversity for improved generalization and robustness. By understanding the impact of different sources of randomness on AGL in foundation models, researchers and practitioners can optimize the training process to enhance the performance and reliability of these models in various tasks and scenarios.

Can the insights on ensemble diversity for observing AGL in foundation models be leveraged to improve their robustness and generalization more broadly

The insights on ensemble diversity for observing Agreement-on-the-Line (AGL) in foundation models can indeed be leveraged to improve their robustness and generalization more broadly. By carefully considering the sources of diversity, such as random head initialization, data ordering, and data subsetting, during the training of foundation models, practitioners can enhance the models' ability to make consistent predictions across different distributions and scenarios. One key implication is that by ensuring diversity in the ensemble, foundation models can better capture the variability in the data and learn more robust representations. This can lead to improved performance on out-of-distribution data, long-tailed distributions, and adversarial perturbations. Additionally, the findings suggest that the choice of randomness in training plays a crucial role in inducing AGL and accurately estimating OOD performance, highlighting the importance of ensemble diversity in model evaluation and deployment. Overall, leveraging ensemble diversity to observe AGL in foundation models can serve as a valuable strategy to enhance their robustness, generalization, and performance across a wide range of tasks and distribution shifts. By optimizing the training process to incorporate diverse sources of randomness, practitioners can improve the reliability and effectiveness of foundation models in real-world applications.
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