Embeddings result from computing what word can appear in a given context, so words that would appear in the same spot will have higher cosine score between themselves.
But it doesn't differentiate further, so you can have "beautiful" and "ugly" embed very close to each other even though they are opposites - they tend to appear in similar places.
Another limitation of embeddings and cosine-similarity is that that they can't tell you "how similar" - is it equivalence or just relatedness? They make a mess of equivalent, antonymous and related things.
But it doesn't differentiate further, so you can have "beautiful" and "ugly" embed very close to each other even though they are opposites - they tend to appear in similar places.
Another limitation of embeddings and cosine-similarity is that that they can't tell you "how similar" - is it equivalence or just relatedness? They make a mess of equivalent, antonymous and related things.