Interpretability Illusions in the Generalization of Simplified Models

Interpretability Illusions in the Generalization of Simplified Models

Interpretability Illusions in the Generalization of Simplified Models is a paper investigating the fidelity of interpretability methods applied to deep learning models, particularly focusing on Transformer models. The paper scrutinizes the assumption that simplified model representations, such as those obtained via singular value decomposition (SVD) or clustering, faithfully capture the behavior of the original models, especially in out-of-distribution (OOD) settings.

Summary of Findings

The paper first addresses simplified representations in the context of deep learning systems. Techniques like dimensionality reduction and clustering are commonly used to make complex model behavior more interpretable. However, the validity of these simplified proxies in predicting the model's behavior, particularly over different data distributions, remains under-explored.

Methodology

The authors examine Transformer models trained on two tasks: a synthetic Dyck language modeling task and a more naturalistic code completion task.

Dyck Language Models:

• Setup: Dyck languages involve the balancing of parentheses, a task with well-defined hierarchical and recursive properties. Transformer models are trained to handle sequences with different depths and structures of nested brackets.
• Simplification: The authors use SVD and clustering to create simplified proxies of the model's key and query embeddings. They then evaluate how closely these proxies replicate the original model’s attention patterns and predictions.
• Results: The simplified models show a high degree of fidelity on in-distribution examples but reveal significant gaps on OOD test data. For instance, while the original models generalize well to unseen structures and deeper nesting, the simplified models do not capture this behavior accurately. Interestingly, some data-independent simplifications, such as a one-hot attention replacement, occasionally outperform the original model but still show notable mismatches in certain OOD settings.

Code Completion:

• Setup: Transformer models are trained on a character-level code completion task using data from CodeSearchNet, which contains functions written in various programming languages.
• Simplification: Similar to the Dyck models, SVD is applied to the key and query embeddings. Performance is evaluated on datasets of Java functions and functions in other languages to assess generalization.
• Results: There are substantial generalization gaps between the simplified and original models, particularly in tasks requiring algorithmic reasoning such as predicting closing brackets or copying variable names. The findings suggest that these tasks involve complex, context-dependent features not captured by simplified representations.

Implications

Practical Implications

The results demonstrate that simplified models, although useful for interpreting in-distribution behaviors, may fall short when dealing with OOD inputs. This has direct implications for the reliability of interpretability methods:

• Interpretability Tools: Methods relying on dimensionality reduction might inadvertently provide an illusory understanding of a model's computation, especially in unseen scenarios.
• Model Safety: Relying on these simplified proxies for safety-critical applications could be problematic, as failure modes may not be anticipated accurately.
• Debugging and Auditing: When interpreting deep models for debugging or auditing, practitioners must be cautious of the limitations highlighted by this study.

Theoretical Implications

The study also raises interesting theoretical questions about the relationship between model complexity and generalization:

• Complexity vs. Generalization: Classical theory suggests simpler models generalize better. However, over-parameterized models like deep neural networks exhibit strong generalization capabilities. Simplified proxies underestimate this generalization, adding nuance to our understanding of complexity and generalization in machine learning.
• Mechanistic Interpretations: Mechanistic interpretations based on simplified circuits or embeddings must be re-evaluated for their robustness across different data distributions. The findings suggest that further work is necessary to ensure these interpretations are not overly reductive.

Future Work

Future research could expand on these findings in several directions:

• Generalization Across More Tasks: Investigating whether these gaps persist in other complex tasks, such as those involving natural language understanding or multi-modal data.
• Larger Models: Analyzing generalization gaps in larger-scale models, such as those used in state-of-the-art natural language processing systems.
• Enhanced Simplification Methods: Developing new simplification techniques that maintain fidelity across a broader range of distributions.
• Circuit-Based Interpretations: Exploring whether understanding entire circuits, rather than individual components, yields more reliable interpretations.

Conclusion

The paper provides a critical analysis of simplified interpretability methods in the context of deep learning, particularly Transformer models. By demonstrating consistent generalization gaps, the study underscores the need for caution when using simplified proxies for interpreting model behavior. These results encourage the continued development of robust, reliable interpretability techniques that faithfully capture model computations across diverse data distributions.