Core Concepts
Position embeddings are used in transformer models to incorporate information about the order of tokens in a sequence, which is not naturally captured by the attention mechanism.
Abstract
The content discusses the concept of position embeddings and the different types of position embeddings used in transformer models.
The key points are:
In recurrent neural networks (RNNs), the hidden state is updated using the current state and past timestamps, which inherently captures the order of the sequence. However, transformers do not naturally grasp the order of a sentence because the attention mechanism calculates relationships between tokens without considering their order.
To address this, researchers introduced position embeddings, which are vectors added to the token embeddings to include information about the order of the tokens in the sequence.
The different types of position embeddings discussed are:
Absolute Positional Embeddings: Vectors with the same dimension as the word embeddings, representing the absolute position of the token in the sequence.
Relative Positional Embeddings: Vectors that capture the relative position of a token with respect to other tokens in the sequence.
Rotary Positional Embeddings: A more efficient way of incorporating positional information by applying a rotation to the token embeddings based on their position.
The author explores the differences between these position embedding techniques and their implications for transformer models.
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