Understanding Single-Headed Attention in Language Models
Are you familiar with language models? If so, you might have come across the term 'Single-Headed Attention' or SHA-RNN. It is a module used in language models that has been designed for simplicity and efficiency. In this article, we will explore what single-headed attention is, how it works, and its benefits.
What is Single-Headed Attention?
Single-Headed Attention (SHA) is a mechanism used in language models to focus on specific parts of input text when generating output text. This is useful to ensure that the generated text is coherent and relevant to the context. SHA only uses one attention head, which is a parameter that defines what part of the input text to focus on. Unlike multi-headed attention, which uses multiple attention heads, SHA is designed to be simple and efficient.
The design of Single-Headed Attention was based on the idea of avoiding running out of memory and considering the lack of significant improvements in performance through multi-headed attention. The main goal of SHA is to reduce computational complexity while keeping the benefits of attention mechanisms to produce better text generation models.
How Single-Headed Attention Works
Single-Headed Attention works by computing a weighted sum of the input representation at each decoder time step. The weight assigned to each input representation is determined by the similarity between the query and the key for each element of the input sequence. The dot product is used to calculate the similarity, and the softmax function is applied to obtain the weights.
The similarity function is defined as:
Where Q is the query, K is the key and d is the dimension of the input vector.
Then, the weights are computed as follows:
Where v is the value, a matrix multiplication between the weights and the values along the sequence is performed to obtain the context vector.
The context vector is then concatenated with the output of the previous decoder time step and passed through a feedforward neural network to produce the final output.
Benefits of Using Single-Headed Attention
Single-Headed Attention has its benefits over multi-headed attention. It is faster and more computationally efficient due to its simple design. It avoids the risk of running out of memory that can occur with multi-headed attention. Furthermore, it can produce equally good results compared to multi-headed attention in many use cases.
Single-Headed Attention has been used in several successful models, such as the Transformer-XL model, which is a state-of-the-art language model. It has shown that Single-Headed Attention can achieve better results compared to the traditional attention mechanism using convolutional neural networks in generating long sequences of text.
The Single-Headed Attention mechanism is a simple and efficient way of using attention in language models. It has shown great results in many use cases and has been used in several successful models. With its efficient design and competitive results, Single-Headed Attention is definitely worth considering over multi-headed attention for your language modelling needs.
