What is the concept of parameter sharing in a large language model?

Parameter sharing is a technique used in large language models to reduce the total number of unique parameters by reusing the same weights across different parts of the model. In transformer architectures, for example, the same set of query, key, and value projection matrices can be shared across multiple attention heads or across layers. This reduces memory usage and computational cost while still allowing the model to learn diverse representations. A common implementation is to share the embedding matrix between the input and output layers, known as tied embeddings, which also improves performance by enforcing consistency between token representations. Parameter sharing can also be applied to entire sub‑modules, such as sharing the same feed‑forward network across layers (as in some recurrent transformer variants). The trade‑off is that while sharing reduces capacity, it often improves generalization and speeds up training, making it a popular choice for scaling models to billions of parameters.