Positional Encoding in Transformers

Why are sine and cosine functions used in positional encoding?

Sine and cosine functions are used because they provide a continuous and differentiable method to encode position information, which helps in training deep learning models. Their periodic nature allows the model to learn and generalize across different positions effectively, and their alternating use across dimensions helps in maintaining unique encodings for each position.

How are positional encodings added to input embeddings?

Positional encodings are added directly to the input embeddings at the base of the Transformer model. This means that each token’s embedding, representing semantic information, is combined with its positional encoding, ensuring that the resulting representation includes both contextual and positional information.

Can Positional Encoding Generalize to Longer Sequences Than Seen During Training?

Yes, positional encoding can effectively handle longer sequences than those encountered during training, thanks to the use of trigonometric functions, which allow for efficient generalization across different sequence lengths.

In the domain of natural language processing (NLP), transformer models have fundamentally reshaped our approach to sequence-to-sequence tasks. .However, unlike conventional recurrent neural networks (RNNs) or convolutional neural networks (CNNs), Transformers lack inherent awareness of token order. In this article, we will understand the significance of positional encoding, which is a critical technique for embedding Transformer models with an understanding of sequence order.