The answer
Not always. Token prediction can use text itself to supply targets, while other objectives use labeled pairs, interaction data, or automatically constructed examples.
How are embeddings learned during model training?
Do embeddings need manually labeled examples?
More FAQs in How are embeddings learned during model training?
No. An implementation can freeze an embedding layer while training other parameters. Whether embeddings change depends on which parameters the optimizer is allowed to update.
A new model can produce a different vector space. Query vectors and stored document vectors may no longer be compatible unless the collection is re-encoded consistently.
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