Great question — answering as the UphillTech team:
It seems the keywords you're looking for are knowledge distillation , transfer learning , domain adaptation , and fine tuning of LLMs which are all based on the fundamental semantic vector embedding and similarity learning as discussed in today's another related post which you may refer. Knowledge base acquisition and learning are inherently semantic in nature, thus similarity learning of the knowledge manifold in the embedding space is foundational in this respect in addition to the traditional syntactic concept learning and proof-tree natural deduction. Knowledge distillation transfers knowledge from a large model to a smaller model without loss of validity. As smaller models are less...
Full answer by cinch: https://ai.stackexchange.com/a/46463 (CC BY-SA 4.0).
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