What is the purpose of using positional information in embedding vectors?

Using positional information in embedding vectors plays a critical role in capturing the ordering of elements within a sequence. In tasks like natural language processing, where the context and arrangement of words are vital for understanding meaning, positional embeddings help the model discern the order of inputs. This is especially important in architectures like transformers, which do not inherently consider the sequential nature of the data.

Attention mechanisms, which are pivotal in transforming and processing sequences, rely on these positional embeddings to understand how different parts of the input relate to one another. By incorporating positional information, the model can compute relationships between pairs of elements in the context of their positions, enabling it to produce more meaningful representations of the input data. This approach significantly enhances the model's ability to recognize patterns based on positional context, improving overall performance in tasks requiring an understanding of sequential patterns.