Absolute Position Encodings: Enhancing the Power of Transformer-based Models
For decades, natural language processing (NLP) models have struggled to outperform human-like accuracy when it comes to understanding and manipulating natural language. In recent years, however, many researchers have been working on improving the power of NLP models by developing better algorithms for word embeddings, such as absolute position encodings.
What are Absolute Position Encodings?
Absolute position encodi
ALiBi, or Attention with Linear Biases, is a new method for inference extrapolation in Transformer models. This method is used instead of position embeddings in computing the attention scores for each head. In other words, ALiBi adds a constant bias to each attention score to simplify calculations and avoid learning the scalar throughout training. The rest of the computation remains unchanged. The following provides more information about this exciting new method.
The Transformer model is widel
What is Conditional Positional Encoding (CPE)?
Conditional Positional Encoding, also known as CPE, is a type of positional encoding used in vision transformers. It is different from traditional fixed or learnable positional encodings which are predefined and independent of input tokens. CPE is dynamically generated and is dependent on the local neighborhood of the input tokens. It has the ability to generalize to longer input sequences than the model has previously seen during training. CPE can
Overview of Relative Position Encodings
Relative Position Encodings are a type of position embeddings used in Transformer-based models to capture pairwise, relative positional information. They are essential in various natural language processing tasks, including language modeling and machine translation.
In a traditional transformer, absolute positional information is used to calculate the attention scores between tokens. However, this approach is limited as it does not differentiate between
What are Rotary Embeddings?
In simple terms, Rotary Position Embedding, or RoPE, is a way to encode positional information in natural language processing models. This type of position embedding uses a rotation matrix to include explicit relative position dependency in self-attention formulation. RoPE has many valuable properties, such as being flexible enough to work with any sequence length, decaying inter-token dependency with increasing relative distances, and the ability to equip linear sel