To build a Large Language Model, the system follows algorithm instructions provided at the input along with training data, whereas, at inference, the LLM is prompted with queries at the input that are designed to achieve the best possible output based on the existing model. Consequently, self prompting in LLMs is thought to be an optimization technique rather than a means of self improvement. Do you support this argument?

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