The Pitfalls of Memorization: When Memorization Hurts Generalization (2412.07684v1)

Abstract: Neural networks often learn simple explanations that fit the majority of the data while memorizing exceptions that deviate from these explanations.This behavior leads to poor generalization when the learned explanations rely on spurious correlations. In this work, we formalize the interplay between memorization and generalization, showing that spurious correlations would particularly lead to poor generalization when are combined with memorization. Memorization can reduce training loss to zero, leaving no incentive to learn robust, generalizable patterns. To address this, we propose memorization-aware training (MAT), which uses held-out predictions as a signal of memorization to shift a model's logits. MAT encourages learning robust patterns invariant across distributions, improving generalization under distribution shifts.

• The paper formalizes how memorization combined with spurious correlations degrades generalization in neural networks.
• It introduces Memorization-Aware Training (MAT) to shift focus from memorized exceptions to invariant, robust patterns.
• Experimental results show that MAT improves worst-group accuracy without needing explicit group annotations, enhancing model robustness.

The Pitfalls of Memorization: When Memorization Hurts Generalization

This work explores a critical challenge within machine learning, particularly focusing on the interplay between memorization and generalization in neural networks. The authors aim to provide a formal understanding of how memorization, often considered a necessary aspect of training, can negatively influence the generalization capability of models, particularly when dealing with spurious correlations.

Neural networks, known for their capacity to model complex functions, often rely on a combination of spurious correlations and memorized exceptions to minimize training loss. While memorization allows a model to achieve impressive training accuracy, this approach does not always translate into robust generalization, especially when the observed patterns during training do not hold in unseen data distributions. The interplay between memorization and generalization becomes crucial when memorized features include spurious correlations—patterns that are artifacts of the training data and do not characterize the underlying true distribution.

Key Contributions

1. Formalizing the Interplay: The authors contribute a nuanced analysis that highlights scenarios where memorization and spurious correlations conspire to impair generalization. Traditional empirical risk minimization (ERM) techniques often exacerbate this issue by using spurious features to achieve zero training loss, neglecting the minority examples that deviate from these features.
2. Memorization-Aware Training (MAT): To mitigate these generalization issues, the paper introduces Memorization-Aware Training (MAT), a novel algorithm that utilizes held-out predictions to adjust model logits during training. This approach shifts the learning focus away from memorizing specific instances and towards capturing invariant patterns that are more resistant to distribution shifts.

Technical Insights and Experimental Evaluation

The paper proposes a controlled experimental setup to illustrate the problem, where the role of memorization and spurious features can be precisely analyzed. The key insights from this setup demonstrate scenarios under ERM where models fail to generalize due to an over-reliance on memorized, non-representative examples.

The theoretical contributions, backed by rigorous proofs, demonstrate the optimal conditions under which memorization and spurious correlations lead to poor generalization. These proofs suggest that when a neural network memorizes exceptions using example-specific features, it misses the opportunity to learn generalizable structures, leading to distribution mismatch and performance degradation in real-world applications.

The experiments highlight MAT's effectiveness compared to existing strategies like Group-DRO, LfF, JTT, and others. On diverse datasets, MAT consistently improves worst-group accuracy, showcasing its ability to curb the adverse effects of memorization. Notably, the results indicate that MAT requires no explicit group annotations during training, amplifying its utility in practical, real-world scenarios where such labels are often unavailable.

Implications and Future Directions

This research underscores the necessity to rethink model training paradigms in modern machine learning, especially in applications where reliance on spurious correlations could have substantial repercussions (e.g., medical diagnostics). While MAT addresses the limitations of ERM, it opens pathways for further explorations into model designs that can balance between memorization and generalization effectively.

For future AI developments, particularly in LLMs, understanding and leveraging memorization without succumbing to its pitfalls will remain critical. The findings suggest promising directions in improving model robustness against dataset biases, promoting fairness, and enhancing generalization under diverse data conditions. This paper lays a foundational framework that not only challenges the existing norms but also propels the field towards intelligent systems capable of learning truly representative features.