Cosine-similarity is a useful metric. The cases where it is useful are models that have been trained specifically to produce a meaningful cosine distance, (e.g.

Cosine-similarity is a useful metric. The cases where it is useful are models that have been trained specifically to produce a meaningful cosine distance, (e.g. OpenAI's CLIP [1], Sentence Tranformers [2]) - but these are the types of models that the majority of people are using when they use cosine distances. > It's my understanding that the delta between two word embeddings, gives a direction, and the magic is from using those directions to get to new words... it's the directions and distances to nearby objects that matters most Cosine similarity is a kind of "delta" / inverse distance between the represenation of two entities, in the case of these models. [1] https://arxiv.org/abs/2103.00020 https://arxiv.org/abs/2103.00020 [2] https://www.sbert.net/docs/training/overview.html https://www.sbert.net/docs/training/overview.html