16th October 2024
A common misconception about Transformers is to believe that they're a sequence-processing architecture. They're not.
They're a set-processing architecture. Transformers are 100% order-agnostic (which was the big innovation compared to RNNs, back in late 2016 -- you compute the full matrix of pairwise token interactions instead of processing one token at a time).
The way you add order awareness in a Transformer is at the feature level. You literally add to your token embeddings a position embedding / encoding that corresponds to its place in a sequence. The architecture itself just treats the input tokens as a set.
Recent articles
- OpenAI DevDay 2026 live blog - 29th September 2026
- 2026 in LLMs (so far) - 27th September 2026
- Claude Opus 5.5, GPT-6 Sol, GPT-6 Luna, and a new price war - 22nd September 2026