Many modern NLP models, especially those based on the transformer architecture, prefer to use relative position embeddings over absolute position embeddings for several reasons:
Flexibility in Sequence Length: Absolute position embeddings assign a unique embedding to each position in the sequence, which limits the model to a fixed sequence length. In contrast, relative position embeddings are independent of sequence length, making the model more flexible and capable of handling sequences of varying lengths.
Generalization: Relative position embeddings allow the model to generalize better to longer sequences that it may not have encountered during training. This is particularly valuable in tasks like machine translation, where the length of the source and target sentences can vary significantly.
Parameter Efficiency: Absolute position embeddings require a separate embedding vector for each position in the sequence, leading to a large number of parameters, especially for long sequences. Relative position embeddings are typically more parameter-efficient because they do not depend on the sequence length.
Reduced Model Complexity: Using relative position embeddings simplifies the architecture of the model because it does not need to learn position-specific embeddings. This can make the model easier to train and interpret.
Positional Information Capture: Relative position embeddings capture relative distances or shifts between positions in the sequence, which can be more important for understanding relationships between words than absolute positions. In many natural language tasks, the relative order of words matters more than their absolute positions.
Attention Mechanism Compatibility: Relative position embeddings are designed to work seamlessly with self-attention mechanisms, which are central to transformer-based models. They enable the model to focus on relevant contextual information by taking into account relative positions within the sequence.
Regularization: Relative position embeddings introduce a degree of regularization by sharing position information across sequences of different lengths. This regularization can help prevent overfitting and improve the model's ability to handle varying sequence lengths.
Transfer Learning: Models with relative position embeddings are more suitable for transfer learning across tasks and domains. They can adapt to different sequence lengths and capture meaningful relationships in the data, making them more versatile.
Overall, the use of relative position embeddings aligns well with the principles of flexibility, parameter efficiency, and improved generalization, which are crucial for building robust NLP models capable of handling diverse text data. Consequently, they have become a standard choice in many state-of-the-art NLP models, including BERT, GPT, and their variants.