Position embeddings in LLMs encode the position of each token in a sequence, enabling the model to understand word order. Transformers process tokens in parallel rather than sequentially, so they require positional information to differentiate between identical words in different contexts. For instance, in “The cat chased the mouse,” position embeddings help the model understand the order of "cat," "chased," and "mouse."
These embeddings are added to or concatenated with token embeddings before being passed into the transformer layers. They can be learned (optimized during training) or fixed (predefined patterns like sine and cosine functions). Fixed embeddings are computationally efficient and ensure that tokens at similar positions have similar positional encodings, aiding in relative position understanding.
Position embeddings are essential for tasks like text generation and language modeling, where word order significantly influences meaning. Without them, models would treat sequences as bags of words, losing the semantic relationships conveyed by token order.