Why do we need positional encoding in transformer-based models?

Positional encoding is a critical component in transformer-based models because, unlike recurrent neural networks (RNNs), transformers process input sequences in parallel and lack an inherent sense of word order. Positional encoding addresses this by embedding information about the position of each token in the sequence, enabling the model to understand the sequential relationships between tokens. According to the original transformer paper ("Attention is All You Need" by Vaswani et al., 2017), positional encodings are added to the input embeddings to provide the model with information about the relative or absolute position of tokens. NVIDIA's documentation on transformer-based models, such as those supported by the NeMo framework, emphasizes that positional encodings are typically implemented using sinusoidal functions or learned embeddings to preserve sequence order, which is essential for tasks like natural language processing (NLP). Options B, C, and D are incorrect because positional encoding does not address overfitting, dimensionality reduction, or throughput directly; these are handled by other techniques like regularization, dimensionality reduction methods, or hardware optimization.