I was referring specifically to popular embedding models like OpenAI’s and sentence-transformers, which (as far as I know) don’t reliably handle negation or emotional nuance, they mostly capture topical similarity.
I don’t know enough of the underlying math to say for sure whether embeddings can be trained to consistently represent negation, but when I tried the Mixedbread demo myself with a query like “winter landscapes without sun and trees”, it still showed me paintings with both sun and trees. So at least in its current form, it doesn’t seem to fully handle those semantic relationships yet.
I don’t know enough of the underlying math to say for sure whether embeddings can be trained to consistently represent negation, but when I tried the Mixedbread demo myself with a query like “winter landscapes without sun and trees”, it still showed me paintings with both sun and trees. So at least in its current form, it doesn’t seem to fully handle those semantic relationships yet.