Positional Encoding | How LLMs understand structure
Positional Encoding in Transformers Explained | How LLMs Understand Word Order
How Rotary Position Embedding Supercharges Modern LLMs [RoPE]
Rotary Positional Embeddings: Combining Absolute and Relative
Detailed Analysis
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Last Updated: September 22, 2026
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Summary
Grant Sanderson of 3Blue1Brown and Alok Puranik, a researcher at Jane Street, work through Alok's latest blog post on What are positional embeddings and why do transformers need Transformers process tokens in parallel — so how do they understand word order? In this video, we explore Timestamps: 0:00 Intro 0:42 Problem with Self-attention 2:30 For more information about Stanford's Artificial Intelligence programs visit: stanford.io/ai This lecture is from the Stanford ... Transformer models can generate language really well, but how do they do it? A very important step of the pipeline is the ... Why can't a Transformer tell "Dog bites Man" from "Man bites Dog"? Because without In this video, I have tried to have a comprehensive look at In this lecture, we deeply understand