This is exactly the challenge. When embedding were first popularized in word to vec they were interpretable because the word2vec model was revealed to be a batched matrix factorization [1].

LLM embedding are so abstract and far removed from a human interpretable or statistical corollary that even as the embeddings contain more information, that information becomes less accessible to humans.

[1] https://papers.nips.cc/paper_files/paper/2014/hash/b78666971...