We’ve Been Doing Attention Wrong (2-Line Fix)

Jia-Bin Huang · Beginner ·🧠 Large Language Models ·3mo ago

About this lesson

Exclusive Self Attention (XSA) proposes a simple yet effective fit to the standard attention mechanism. With only 2 lines of code, XSA improves the overall performance without incurring significant compute and memory costs. 00:00 Introduction 00:18 Attention layer 01:46 Feedforward network 03:05 Why can't we just ignore the self-value vector? 04:41 Attention similarity bias 05:38 Visualization of orthogonalization 07:50 Implementation of XSA 09:09 Performance 10:25 Robustness to hyperparameters 11:52 Summary Reference: - Exclusive Self Attention https://arxiv.org/abs/2603.09078 Video made with manim: https://www.manim.community/

Original Description

Exclusive Self Attention (XSA) proposes a simple yet effective fit to the standard attention mechanism. With only 2 lines of code, XSA improves the overall performance without incurring significant compute and memory costs. 00:00 Introduction 00:18 Attention layer 01:46 Feedforward network 03:05 Why can't we just ignore the self-value vector? 04:41 Attention similarity bias 05:38 Visualization of orthogonalization 07:50 Implementation of XSA 09:09 Performance 10:25 Robustness to hyperparameters 11:52 Summary Reference: - Exclusive Self Attention https://arxiv.org/abs/2603.09078 Video made with manim: https://www.manim.community/
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Chapters (10)

Introduction
0:18 Attention layer
1:46 Feedforward network
3:05 Why can't we just ignore the self-value vector?
4:41 Attention similarity bias
5:38 Visualization of orthogonalization
7:50 Implementation of XSA
9:09 Performance
10:25 Robustness to hyperparameters
11:52 Summary
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