AI Briefing#self-attention
The latest AI and developer news about #self-attention, with the original source and a short summary.
The latest AI and developer news about #self-attention, with the original source and a short summary.
This analysis explores the background of AI translation becoming commonplace and how the LLM's Decoder-Only structure differs from existing NMT.
Introduces various implementation methodologies for improving the computational efficiency of self-attention, the core of the Transformer.
By decomposing self-attention weights, editing directions for diffusion were found without any additional training.
LLM inference splits into prefill and decode, and the KV cache determines speed.
We introduce Nyströmformer, which uses the Nyström method to reduce the complexity of Transformer's self-attention from $O(n^2)$ to $O(n)$.