Unlocking the Secrets of Interpretability: A Modern Approach

**Unveiling the Evolutionary Process: The Pitfall of Interpretability Methods**

**Introduction: The Story of Cuckoo Birds and the Evolutionary Perspective**

Throughout history, Europeans believed that a cuckoo bird laying its egg in another bird’s nest was a sign of honor and an opportunity to demonstrate Christian hospitality. The devoted host bird would enthusiastically care for the cuckoo egg as if it were her own, often neglecting her own chicks in the process. However, Charles Darwin’s study of finches in 1859 challenged this cooperative view of bird behavior. By considering the evolutionary perspective, we can understand that the nesting bird is not a gracious host, but rather a victim of deception. This illustrates the importance of understanding the biological consequences of the evolutionary process. In the world of machine learning, a similar understanding is crucial when analyzing the behavior of neural networks. By examining the training process, we can distinguish between a compelling story and a meaningful analysis. In this article, we will explore the concept of “interpretability creationism” and the need to focus on the training process in interpretability research.

**The Significance of Interpretability Creationism**

As humans, we tend to seek causal explanations even when we don’t fully understand the underlying processes. This is evident in pre-evolutionary folk tales that explain animal traits through just-so stories, offering purpose or cause without considering the scientific basis of evolution. In the field of natural language processing (NLP), researchers often propose interpretable explanations for observed behavior without taking into account the development of that behavior. For example, researchers may identify specific patterns of behavior in a model, such as syntactic attention distributions or selective neurons. However, how can we be certain that these patterns are actually utilized by the model? To test the influence of specific features and patterns, interventions can be performed, but these may only target explicit behaviors and fail to capture the true interactions between features. Moreover, interventions can introduce distribution shifts that the model may not be robust against, leading to spurious interpretable artifacts. Often, incidental observations are mistakenly perceived as essential.

**The Importance of Evolutionary Analysis**

Fortunately, the study of evolution provides valuable insights into interpreting the artifacts produced by models. Similar to vestigial features in biology, artifacts can lose their original function and become obsolete during the training process. Some artifacts may rely on the presence of other properties earlier in training, while others may compete with each other for dominance. Additionally, some artifacts may be mere side effects of training without impacting the core strategy of the model. The emergence of unused artifacts during training can be explained by the Information Bottleneck Hypothesis, which suggests that models initially memorize inputs before compressing them to retain only relevant information about the output. Vestigial features can be observed in language models, where early models behave similarly to ngram models and gradually develop more complex linguistic patterns. Therefore, understanding the evolutionary process can help differentiate between crucial components of a trained model and incidental artifacts.

**A Case Study in Interpretability Creationism**

To further illustrate the pitfalls of interpretability creationism, let’s consider an example involving text classifiers trained with different random seeds. These models were found to cluster in distinct groups, and the generalization behavior of a model could be predicted based on its connections in the loss surface. However, it was critical to determine whether these clusters were represented at earlier stages of the training process. If this were the case, the only significant finding would be that some training runs were slower than others. To validate the claim that models can become trapped in basins on the loss surface, the evolution of training trajectories needed to be examined. This analysis revealed that models located centrally within a cluster became more strongly connected to the rest of their cluster over time. However, some models successfully transitioned to a better cluster. By studying the evolution of behavior, the hypothesis regarding the diversity of generalization behavior in trained models was confirmed.

**A Proposal: Emphasizing the Training Process in Interpretability Research**

It is important to acknowledge that not every question can be answered solely by observing the training process. Causal claims require interventions that go beyond passive observation. Just as researchers deliberately expose bacteria to antibiotics in biology experiments, the claims based on training dynamics may also require experimental confirmation. However, analyzing static models can still provide valuable insights in certain cases. For example, by examining the behavior of specific neurons or identifying accessible information within a model, simple claims can be made. To achieve a comprehensive understanding, both the training process and the final state of the model must be considered.


In the quest for interpretability in machine learning models, it is crucial to avoid the pitfall of interpretability creationism. Merely analyzing the final state of a model without considering its evolutionary journey can lead to misleading interpretations. By understanding the biological consequences of the evolutionary process, we can differentiate between meaningful analyses and mere artifacts. Incorporating the training process in interpretability research allows for a more comprehensive understanding of model behavior. Through the lens of evolution, we gain invaluable insights into the development and functionality of machine learning models.

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