What role do activation functions play in neural networks used for vector embeddings?

Activation functions are crucial components in neural networks because they introduce non-linearity into the model. This non-linearity allows the network to learn complex patterns in the data, enabling it to model functions that are not merely linear combinations of inputs. In the context of vector embeddings, which aim to encapsulate the semantics of the input data in a compact form, the introduction of non-linear activation functions helps to create richer representations. These functions allow the network to combine and transform embeddings in ways that reflect intricate relationships and structures within the data. Without activation functions, the model would effectively behave as a linear classifier, limiting its ability to capture the full spectrum of relationships inherent in complex datasets.