When Transformers Sing: Adapting SpectralKD for Text-Based Knowledge Distillation

This piece explores adapting SpectralKD, a technique from computer vision, for text-based knowledge distillation in Transformer models. It details how Fast Fourier Transform (FFT) can analyze the "spectral intensity" of a model's layers to identify which ones are most informative. This frequency analysis helps distinguish between layers that capture broad, semantic structures and those that focus on fine-grained, token-level details. By using this method to select the most information-rich layers from a teacher model for distillation, the author successfully improved a student model's performance on an intent classification task.