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Transformer

Model ● Covered in 6 stories + Follow

The Transformer is a model architecture introduced in 2017 that uses multi-head self-attention mechanisms for natural language processing tasks. Recent research has focused on improving its efficiency and capabilities through variants like Parcae (which reuses layers for parameter efficiency), Mamba (which replaces attention with state space models for faster inference), and Nyströmformer (which approximates attention in linear time), while unsupervised pre-training combined with Transformers has demonstrated strong performance across language understanding benchmarks.

Updated 4 August 2026

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Latest developments

Parcae: Doing more with fewer parameters using stable looped models Together AI · 5 months ago · 14
Mamba Explained The Gradient · 2 years ago · 32
Nyströmformer: Approximating self-attention in linear time and memory via the Nyström method Hugging Face · 4 years ago · 4
NLP for Supervised Learning - A Brief Survey Eugene Yan · 6 years ago · 48
The Transformer Family Lil'Log · 6 years ago · 48
Improving language understanding with unsupervised learning OpenAI · 8 years ago · 24

Timeline

Month Quarter Year

April 2026

March 2024

August 2022

August 2020

April 2020

June 2018

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