Positional Encoding in Transformers

About this lesson

This video offers a comprehensive deep dive into the concept of Positional Encoding (PE) within the Transformer Architecture. We begin by answering why PE is necessary: because the self-attention mechanism processes words in parallel, it inherently lacks any sense of order or sequential understanding, which is critical for NLP tasks. Without positional information, a Transformer cannot distinguish between sentences with the same words but different meanings, such as "The cat chased the mouse" and "The mouse chased the cat". We first examine the challenges of using naive approaches, such as assigning discrete position indices (like 1, 2, 3...). These discrete numbers are problematic for training neural networks and can become large and unbounded for long sentences, leading to unstable gradients and potential vanishing or exploding gradient problems. Normalizing these values also fails because it leads to inconsistent position representations across sentences of different lengths. The elegant solution proposed by the original authors is the use of sinusoidal functions (sine and cosine). These functions generate continuous values that are bounded (between -1 and 1), ensuring numerical stability during training. Positional encoding is represented as a high-dimensional vector, created using pairs of sine and cosine functions with progressively decreasing frequencies. This strategy guarantees that each position receives a unique vector representation, preventing collisions even in extremely long sentences. The positional encoding vector has the same dimension as the word embedding vector. To integrate this positional data without drastically increasing the model's parameters or slowing down training, the positional encoding vector is combined with the word embedding using element-wise addition. This technique preserves the model's efficiency while ensuring that the semantic information and the positional information remain distinguishable by the model. Finally, we explore

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