Understanding Embeddings: The Hidden Power Behind Language Models
Large Language Models like GPT, Claude, and Gemini rely on embeddings — dense vector representations of words — to generate accurate and context-aware responses. This post explores the history of embeddings from early methods like N-grams and One-Hot Encoding to the breakthrough of Word2Vec, and explains why embeddings are key to enabling AI tools to understand and process language effectively.
Byamasu Patrick Paul
9 min read
Large Language Models like GPT, Claude, and Gemini rely on embeddings — dense vector representations of words — to generate accurate and context-aware responses. This post explores the history of embeddings from early methods like N-grams and One-Hot Encoding to the breakthrough of Word2Vec, and explains why embeddings are key to enabling AI tools to understand and process language effectively.
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Cited Research
Mikolov, T., et al. (2013). "Efficient Estimation of Word Representations in Vector Space." arXiv:1301.3781.
Vaswani, A., et al. (2017). "Attention Is All You Need." Proceedings of NeurIPS 2017.
Devlin, J., et al. (2018). "BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding." arXiv:1810.04805.