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This chapter discusses mathematical models underlying word embeddings and positional encodings in transformers. It covers classic techniques like Word2Vec and GloVe, other advanced embeddings, and sinusoidal and learned positional encodings ([3–5]). The integration of embeddings and encodings is analyzed for its impact on self-attention and model performance.

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Word Embeddings and Positional Encoding

  • 章节

  • 发表日期：

    2025年

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  • Pradeep Singh,
  • Balasubramanian Raman

摘要

This chapter discusses mathematical models underlying word embeddings and positional encodings in transformers. It covers classic techniques like Word2Vec and GloVe, other advanced embeddings, and sinusoidal and learned positional encodings ([3–5]). The integration of embeddings and encodings is analyzed for its impact on self-attention and model performance.