AI BriefingLG AI Research 222
Key point
Introduces various implementation methodologies for improving the computational efficiency of self-attention, the core of the Transformer.
Details
Transformer effectively captures the relationships between words within a sentence through the self-attention mechanism, and serves as a backbone in NLP and various other fields.
Existing RNN-based models had drawbacks such as the long-term dependency problem that arises when passing time-series information, and difficulty with parallel processing, but Transformer overcame these issues and demonstrated superior performance.
However, self-attention has a limitation in that it requires $O(N^2)$ computation with respect to the input sequence length ($N$), making it inefficient when processing long sequences. To address this, the following approaches are proposed.
- Approximation: Instead of computing all attention weights, only the values with high importance are computed (e.g., Sparse attention)
- Other efficient implementation methods: Reduce computation so that it can be utilized even in applications that require long sequence inputs.
This summary was generated automatically by AI. Check the original for the author's claims and context. Copyright belongs to the original author.
Our guide explains how the AI works. Report summary errors, attribution issues, or removal requests via Contact.
Related
Microsoft's Full-bandwidth Transformer