Positional Encodings: A Key Ingredient for Transformer Models
This article discusses the importance of positional encodings in transformer models, which allow them to understand word order and sequence information.
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Why it matters
Positional encodings are a fundamental component of transformer models, enabling them to understand and process language effectively.
Key Points
- 1Transformers do not inherently understand word order without positional information
- 2Naive approaches to adding position information can either overpower meaning or make training harder
- 3Positional encodings quietly teach models that order matters, enabling them to distinguish between sentences with different word orders
- 4Positional encodings are a small but crucial detail that have a huge impact on transformer model performance
Details
The article explains that without positional information, transformer models see text as a bag of tokens, unable to distinguish between words in different positions. For example, the model would not be able to tell the difference between
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