<ul><li>Large Language Models (LLMs), built on Transformer architectures, exhibit remarkable generalization across a wide range of tasks.</li><li>Fine-tuning LLMs for specific tasks remains resource-intensive due to extensive parameterization.</li><li>Two remarkable phenomena related to the attention mechanism during fine-tuning of LLMs are investigated.</li><li>Insights from the investigation lead to a new strategy to improve fine-tuning efficiency in terms of storage and time.</li></ul>

Theoretical Insights into Fine-Tuning Attention Mechanism: Generalization and Optimization

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