DBA: Efficient Transformer With Dynamic Bilinear Low-Rank Attention

Area of Science:

• Artificial Intelligence
• Machine Learning
• Natural Language Processing

Background:

• Transformer models are computationally intensive, motivating research into efficiency improvements.
• Existing low-rank methods for Transformer efficiency have limitations in handling sequence-specific variations and optimizing hidden state dimensions.

Purpose of the Study:

• To propose Dynamic Bilinear Low-Rank Attention (DBA), an efficient attention mechanism addressing limitations of prior methods.
• To achieve linear time and space complexity while maintaining state-of-the-art performance in Transformer models.

Main Methods:

• DBA utilizes input-sensitive dynamic compression matrices to compress sequence length.
• It jointly optimizes sequence length and hidden state dimension using bilinear forms.
• Theoretical analysis based on low-rank matrix properties and the Johnson-Lindenstrauss lemma supports the approach.

Main Results:

• DBA achieves state-of-the-art performance across various tasks and sequence lengths.
• The method demonstrates significant improvements in processing speed and reductions in memory usage.
• Experimental validation confirms the effectiveness of dynamic compression coefficients and hidden state dimension approximation.

Conclusions:

• DBA offers an efficient and effective solution for enhancing Transformer model performance.
• The dynamic, input-sensitive approach overcomes limitations of fixed compression methods.
• DBA presents a promising direction for efficient deep learning architectures, particularly in natural language processing.