Understanding Transformer Circuits: A Mechanistic Interpretability Perspective
By
cjamsonhn
Front-window bakery material. Catches the eye, delivers the goods.
Summary
This article explores mechanistic interpretability of transformer neural networks, focusing on understanding how transformers work mathematically by examining the residual stream and attention mechanisms. The author shares insights gained from studying 'A Mathematical Framework for Transformer Circuits' and working through mechanistic interpretability exercises, aiming to develop intuition about transformer circuits and help others understand complex concepts in the field.
Key quotes
· 4 pulledMy goal is to describe my current intuition for the paper, especially parts I was confused about so that perhaps my take can help others gain clarity on these areas as well.
First, a br
In a previous post on language modeling, I implemented a GPT-style transformer. Lately I've been learning mechanistic interpretability to go deeper and understand why the transformer works on a mathematical level.
This post is a brain dump of what I've learned so far after reading A Mathematical Framework for Transformer Circuits (herein: 'Framework') and working through the Intro to Mech Interp section on ARENA.
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