Imagine trying to understand a complex machine by looking only at the inputs and outputs. That's the challenge researchers face with transformers, the powerful engines behind large language models (LLMs). These models excel at generating human-like text, yet we still don't fully grasp how they process information. A new research paper, "Understanding Transformers via N-gram Statistics," sheds light on this mystery by examining how transformers utilize simple statistical patterns called N-grams. N-grams are sequences of 'N' words—think "happily ever after" (a 3-gram) or "the cat sat on the mat" (a 6-gram). The research focuses on how well these basic statistical rules can approximate what a transformer does. Surprisingly, the results reveal a fascinating connection between the consistency of a transformer's predictions and how well they can be described by N-grams. When a transformer produces similar outputs across multiple training runs with different data shuffles (low variance), its predictions are more likely to align with N-gram rules. This suggests that transformers initially learn simpler patterns and progressively incorporate more complex ones as training progresses, much like a student mastering basic arithmetic before tackling calculus. This observation has practical implications. By analyzing the alignment between transformer predictions and N-gram rules, the researchers propose a novel way to detect overfitting, a common issue where a model performs well on training data but poorly on unseen data. This new method, unlike traditional techniques, doesn't require a separate validation dataset, streamlining the training process. The most striking finding is how well N-grams can mimic transformer behavior. The study found that for a significant portion of predictions on simple datasets, the transformer's top choice matched that of the N-gram rules. This reinforces the idea that even complex models rely heavily on the statistical structure of their training data. While this research focuses on simplified scenarios, it offers a glimpse into the intricate workings of transformers. Future work could extend these insights to more complex datasets and models, paving the way for a deeper understanding of LLMs and unlocking their full potential.