Why Positional Encoding Matters

Without a way to encode the position of each token, a Transformer would perceive the input as a bag of words, losing critical information about sentence structure and meaning. For instance, the sentences "the cat chased the mouse" and "the mouse chased the cat" contain the same words but convey entirely different meanings due to word order. To address this, positional encoding is introduced to inject information about the order of tokens into the model, allowing it to distinguish between different arrangements of the same words and thus better understand the context and meaning of text.

There are multiple strategies for adding positional information to token embeddings in Transformers. The two most common are sinusoidal and learned positional encodings. Each approach has unique characteristics and trade-offs, especially when applied to various NLP tasks.