For word-level word embeddings this means that there are more vectors from the same volume of text, that they are built with less information, and that there are more vectors that are very similar (which complicates the task for a method relying on cosine similarity).
it could be argued that machine-learning-based methods should not be used for benchmarking because they could learn to ignore noise too well.