4meta_forgetting_gpt

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The method’s effectiveness stems from its precision: broad regularization increases train loss by 237%for

comparable speed gains, while our targeted approach limits loss increase to 8.6% through layer-wise sensitivity analysis. This

demonstrates that strategic parameter forgetting can enhance efficiency without compromising linguistic capability, offering a pathway

to more adaptable and maintainable language models.The method’s effectiveness stems from its precision: broad regularization increases train loss by 237%for

comparable speed gains, while our targeted approach limits loss increase to 8.6% through layer-wise sensitivity analysis. This

demonstrates that strategic parameter forgetting can enhance efficiency without compromising linguistic capability, offering a pathway

to more adaptable and maintainable language models.getting in anguage Models:Learning to Discard for Enhanced Efficiency and Generalizationsdf The method’s effectiveness stems from its precision: broad regularization increases train loss by 237%for

comparable speed gains, while our targeted approach limits loss increase to 8.6% through layer-wise sensitivity analysis. This

demonstrates that strategic parameter forgetting can enhance efficiency without compromising linguistic capability, offering a pathway

to more adaptable and maintainable language models.