It's more common to refer to embeddings as low-dimensional.
I've rarely seen embeddings with fewer than hundreds of dimensions.
UMAP/T-SNE are dimensional reduction techniques that could maybe considered embeddings, but I haven't encountered that in anything that relates to word2vec or LLMs or much of the current AI fashion.
But it is a vector space. Technically the one-hot elements are the basis of the vector space, whose elements are weighted lists of words.
And it is the vector space with the highest useful dimension for embedding that vocabulary. Seriously, what are you going to do with the extra axes if you're representing a 10,000-word vocabulary using 1,000,000-element vectors? Those extra axes simply go to waste. In the one-hot case the only thing wasted is the magnitudes.
This is why word2vec embeddings are considered lower-dimensional embeddings. They're lower-dimension than the most-naive-but-not-wasteful representation.
While I would agree that technically, in terms of raw dimensionality, a one-hit encoding is larger dimension than the embedding, that would be a "lower" dimensionality, not "low" dimensionality. Who uses "low" as an absolute term when referring to 300-2000 dimensions?
> I tend to think
> I havent encountered
Egocentric views of things are far less notable and far less interesting than what those things actually are. It makes for tiring reading. Hope it does not come off as being too testy, my apologies if it does.
I dont doubt that for a minute and neither did I intend such an interpretation.
Perhaps if I were to debate a well known and well understood concept in epigenetics in terms how I personally think about it (with not even an iota of intent to boost my ego), my comment above might resonate better. For the purpose of such a hypothetical debate, how I personally think about that concept in epigenetics, becomes more of a distraction. Does it not ?
No offense intended and none taken. I do learn a lot from your comments, especially about the intricacies of biology.