Embeddings are underrated (2024)

Question for the experts: As embeddings in question reflect content is there another vector for style? I was wondering if stylometry research would profit from embeddings becoming more easily accessible than ever. I dug my head into this matter some time ago and believe this would be the right tool.

If you have a dataset for which you know which elements have similar/same style, you can create your own embeddings for style.

Embeddings as a tool have been around for longer than LLMs. They were (and are) ubiquitous in, e.g., recommender systems. It sounds maybe this would be more in-line with what you are looking for. In this case, check out https://github.com/benfred/implicit - I have used it in the past with great success.