Comment by bagels

I was curious what GPT 4 would answer with that prompt:

input: king - man + woman =

"The equation you're referring to is often used as an example of how word embeddings work in natural language processing (NLP). Word embeddings are a type of word representation that allows words to be represented as vectors in a continuous vector space. This allows for capturing semantic similarities between words based on their context.

The equation "king - man + woman" is a famous example that illustrates how word embeddings can capture analogies. In vector space, the operation is meant to find a word that is similar to "king" but more closely associated with "woman" than "man". The result of this operation is often "queen".

This result demonstrates the ability of certain word embedding models, like Word2Vec or GloVe, to capture relational similarities between words. It's a reflection of the model's capacity to understand that the relationship between "king" and "man" is similar to the relationship between "queen" and "woman", based on the learned vector representations of these words from large text corpora."