Fine-Tuning LLMs is a Huge Waste of Time

Fine-tuning advanced LLMs for knowledge injection is counterproductive because it overwrites existing knowledge rather than adding new information—neurons are finite resources where updating weights risks erasing the intricate patterns already encoded in the network. Instead of fine-tuning, modular techniques like retrieval-augmented generation, adapters, and prompt engineering should be used to inject new knowledge without compromising the model's carefully built foundational ecosystem.

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