One of the applications of word embeddings (such as GloVe) is finding words of similar meaning. I just had a look at some embeddings produced by glove on large datasets and I found that the nearest neighbors of a given word are often fairly irrelevant. Eg. ‘dad’ is the closest neighbor of ‘mom’, ‘dealership’ is the seventh closest neighbor of ‘car’.
In light of this, if you wanted to find words of similar semantics why would you prefer using embeddings instead of just downloading a database of synonyms from an online dictionary that is compiled by humans?