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In practical applications of fine-tuning, the model's parameters, denoted as $$\theta$$, are initialized with the values from the pre-trained model. The subsequent adjustments to these parameters are intentionally moderate to ensure that the fine-tuned model retains the core knowledge from pre-training and does not deviate excessively from its original capabilities.

Parameter Initialization and Moderate Adjustment in Fine-Tuning

Based on the provided scenario, what is the most likely cause of the model's performance degradation on general tasks, and what specific aspect of the training process should be adjusted to mitigate this issue?

Diagnosing Fine-Tuning Performance Degradation

A developer fine-tunes a large, pre-trained language model on a small, specialized dataset. After training, they find the model achieves near-perfect accuracy on the specialized task but has significantly lost its ability to answer general questions it previously handled well. Based on the principles of parameter adjustment during fine-tuning, critique the developer's likely training approach and explain why this performance trade-off occurred.

Critiquing a Fine-Tuning Strategy

A machine learning engineer is adapting a large, pre-trained language model for a highly specialized task using a small dataset. They choose an aggressive training strategy that results in substantial changes to the model's parameters from their initial, pre-trained values. Which of the following outcomes represents the most significant risk of this approach?

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