Your question is Positional Embeddings Comparison. Take a moment with it on the right.
Talk me through your thinking if you like. When you're confident, submit your answer and I'll grade it like a real screen (7/10 or better passes).
What is the difference between using trainable positional embeddings derived from a patch-CNN versus the non-parametric positional embeddings used in the original "Attention is All You Need" paper?
Explain the distinction and implement both approaches in PyTorch for token sequences. Show how positional information is generated, identify the main optimization and generalization trade-offs, and propose an evaluation that isolates positional encoding quality rather than overall language-model capacity. Discuss sequence-length extrapolation, computational cost, parameterization, and the risk of content-dependent positional representations.