Are nearby embeddings always semantically similar?
Only when the model and training objective make that distance meaningful. Some embeddings reflect interaction patterns, labels, or other task-specific relationships instead of general semantic similarity.
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More FAQs in What is an embedding?
It is a representation that may preserve useful information, but it is not generally a lossless encoding from which the original text can be recovered exactly.
Direct comparison is usually invalid because the models can define different coordinate spaces. Re-encode the collection consistently or use an explicitly trained alignment method.
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