GloVe (Global Vectors for Word Representation) creates word embeddings by factorizing a global word co-occurrence matrix. It combines global corpus statistics with local context to generate dense vectors. In the article's context, GloVe illustrates early embedding methods before attention-based models. Like Word2Vec, it outputs static embeddings, which are later passed into attention mechanisms. GloVe's design helps relate semantic similarity numerically. Understanding GloVe supports the article's explanation of input processing. It shows how prior work informed the development of self-attention architectures.