Summary:
- The article discusses the limitations of using benchmarks to evaluate machine learning models, and advocates for a more nuanced approach called "ablation studies."
- Ablation studies involve systematically removing or modifying components of a model to understand their individual contributions to the overall performance, providing deeper insights into the model's inner workings.
- The author argues that this approach can lead to more robust and interpretable models, as well as a better understanding of the underlying problem being solved, rather than simply optimizing for a specific benchmark metric.