What is the purpose of the positional encoding in a language model?

The purpose of positional encoding in a language model is to provide each token in the input sequence with information about its position. This is crucial because unlike recurrent neural networks (RNNs), which inherently process tokens in a sequential manner, transformer models process all tokens in parallel. Without a way to indicate the relative positions of tokens, the model would treat the input as a bag of words, losing the important sequential context that influences meaning and structure in language.

Positional encodings are added to the input embeddings to maintain the order of the sequence, allowing the model to understand the relationships between tokens based on their positions. This is vital for language understanding and generation tasks, where the meaning of a word can change significantly depending on its position in a sentence or phrase. The use of sinusoidal functions or learned encodings helps achieve this role effectively, ensuring that the transformer can correlate elements in the sequence accurately.