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Word embeddings are directly responsible for many of the exponential advancements natural language technologies have made over the past couple years. They’re foundational to the functionality of ...
A picture may be worth a thousand words, but how many numbers is a word worth? The question may sound silly, but it happens to be the foundation that underlies large language models, or LLMs — and ...
This post explores how bias can creep into word embeddings like word2vec, and I thought it might make it more fun (for me, at least) if I analyze a model trained on what you, my readers (all three of ...