AttentionQKV

Attention QKV (QKV stands for Queries, Keys, and Values) is a variation of self-attention mechanism, a key component of transformer models. In standard self-attention, queries, keys, and values are typically learned as different projections of the same input embedding space, using a specific linear transformation for each. However, in Attention QKV, the queries, keys, and values are dissociated from each other, projecting onto three separate embedding spaces.

This approach differs significantly from the traditional self-attention mechanism where the queries, keys, and values are linearly transformed versions of the same input embeddings. The dissociation of queries, keys, and values utilises three separate embedding spaces, offering more flexibility in neural network design. The advantage is that more complex relationships between different dimensions of the input data can be modeled by leveraging the additional orthogonality between queries, keys, and values.

The attention QKV mechanism is part of the transformer architecture modifications that seek to improve the performance and the efficiency of the model. In particular, the QKV dissociation represents a significant innovation in deep learning architectures, revolutionising how self-attention works in transformer-based models. The approach may be seen as a refinement of the original self-attention, a possibility to extend the capabilities of the transformers even further.

The development of Attention QKV can be understood as a continued effort to fine-tune the performance of transformer-based models, delving deeper into the intricacies of self-attention. This mechanism has been explored in various domains and made considerable impact on applying transformer-based models effectively.