DeepSeek manages overfitting during fine-tuning by employing a combination of techniques that help maintain the model's generalization capabilities while adapting it to specific tasks or datasets. Overfitting occurs when a model learns the noise and peculiarities of the training data rather than the underlying patterns, resulting in poor performance on unseen data. To combat this, DeepSeek uses strategies such as regularization, data augmentation, and early stopping.
Regularization techniques, such as L1 and L2 regularization, are common practices to mitigate overfitting. In DeepSeek, these methods penalize large weights in the model by adding a term to the loss function that discourages complexity. This helps the model to focus on learning essential features from the data rather than memorizing it. By adjusting the regularization coefficients, DeepSeek can control the balance between fitting to the training data and maintaining generalization to new data.
Data augmentation is another critical approach used during fine-tuning in DeepSeek. This involves artificially increasing the size of the training dataset by applying transformations such as rotations, shifts, or noise. For example, if the original dataset consists of images, different variations of the same image can be generated. This helps the model learn to be robust to variations and noise in the input data, thereby reducing the likelihood of overfitting. Lastly, early stopping is employed to monitor the model's performance on a validation set during training. If the performance begins to degrade, training is halted, which helps prevent the model from overfitting to the training data. Together, these methods ensure that DeepSeek remains effective and generalizes well across different tasks and datasets.