Linear Connectivity Reveals Generalization Strategies (2205.12411v5)

Abstract: It is widely accepted in the mode connectivity literature that when two neural networks are trained similarly on the same data, they are connected by a path through parameter space over which test set accuracy is maintained. Under some circumstances, including transfer learning from pretrained models, these paths are presumed to be linear. In contrast to existing results, we find that among text classifiers (trained on MNLI, QQP, and CoLA), some pairs of finetuned models have large barriers of increasing loss on the linear paths between them. On each task, we find distinct clusters of models which are linearly connected on the test loss surface, but are disconnected from models outside the cluster -- models that occupy separate basins on the surface. By measuring performance on specially-crafted diagnostic datasets, we find that these clusters correspond to different generalization strategies: one cluster behaves like a bag of words model under domain shift, while another cluster uses syntactic heuristics. Our work demonstrates how the geometry of the loss surface can guide models towards different heuristic functions.

• The paper reveals that text classifiers can reside in distinct loss basins, leading to non-linear connectivity and varied generalization strategies.
• The paper introduces the convexity gap metric to cluster models and predict their basin membership early in the training process.
• The paper challenges conventional transfer learning assumptions by showing that fine-tuning from pretrained models may yield inconsistent performance under domain shifts.

Analysis of Linear Connectivity in Text Classifiers and its Implications for Model Generalization

In the research paper "Linear Connectivity Reveals Generalization Strategies," the authors explore the concept of Linear Mode Connectivity (LMC) within neural networks, focusing specifically on text classifiers trained on datasets such as MNLI, QQP, and CoLA. The paper challenges the assumed linear connectivity of models across different tasks, a concept widely accepted in image classification. By examining how clusters of models exhibit variations in generalization strategies, the authors provide new insights into how the geometry of the loss surface affects model performance under domain shifts.

Key Findings

1. Non-linear Connectivity in Text Classifiers: Contrary to results in image classification, the paper finds that text classifiers do not universally demonstrate LMC. Despite similar training conditions, models can reside in distinct basins within the loss landscape, resulting in non-linear connectivity where linear paths between models display increasing loss barriers.
2. Distinct Generalization Strategies: The distinct basins correlate with different generalization strategies. In the context of MNLI, for example, one cluster of models functions akin to a bag of words strategy under domain shift, while another leverages syntactic heuristics more effectively. This division is highlighted through cluster-specific behaviors observed on diagnostic sets such as HANS.
3. Implications for Transfer Learning: The findings suggest that conventional assumptions about LMC in transfer learning may not hold broadly. The presence of multiple basins indicates that fine-tuning from a common pretrained model does not always guarantee consistent generalization performance across training runs.
4. Convexity Gap as a Metric for Model Similarity: The paper introduces a new metric, the convexity gap (CG), to measure model similarity based on LMC. This metric proves effective in clustering models into basins that align with specific generalization strategies.
5. Early Predictive Indicators: Interestingly, the paper reveals that basin membership—and consequently, a model's generalization strategy—can be predicted early in the training process. Models are observed to become increasingly trapped in specific basins as training progresses.

Theoretical and Practical Implications

The paper presents significant implications for both theoretical understanding and practical applications in AI. Theoretically, it questions the assumption that models always occupy a single connected basin irrespective of the task, highlighting the need for more nuanced perspectives on loss surface topology as it applies to NLP tasks. Practically, the findings suggest potential avenues for enhancing model development, such as devising strategies to navigate or influence basin selection during training, potentially leading to more robust and reliable models under domain shifts.

Moreover, understanding generalization strategies through linear connectivity offers a new lens for evaluating model robustness and adaptability, which is critical for real-world applications of NLP systems that encounter varying inputs and contexts.

Speculation on Future Developments

Looking ahead, this research paves the way for further investigations into the impact of architectural choices and training protocols on basin formation. There is potential for developing predictive techniques to identify desired generalization strategies early in training, optimizing models for tasks that involve significant domain shifts. Additionally, extending this line of inquiry to other NLP settings beyond binary classification tasks may uncover more complex relationships between loss surface geometry and model behavior.

In conclusion, this paper challenges existing paradigms around LMC and provides a compelling framework for understanding the intricate dynamics of neural network training in text classification. By dissecting the nature of connectivity and its role in generalization, the paper lays the groundwork for future advancements in model fine-tuning and transfer learning across diverse data domains.