The "drifting away" phenomenon observed during prolonged additional pretraining, where the performance of the model starts to decline after a certain point, can be mitigated through several strategies:
Regular Evaluation: Continuously evaluating the model's performance during additional pretraining can help in identifying the point at which the performance starts to deteriorate. By monitoring the model's progress, adjustments can be made to prevent significant performance drops.
Balanced Data: Ensuring a balanced mix of data from the target language and other languages during pretraining can help maintain the multilingual aspect of the model. By incorporating data from various languages, the model can retain its ability to understand and process multiple languages effectively.
Optimized Hyperparameters: Fine-tuning hyperparameters such as learning rate, batch size, and number of training steps can play a crucial role in preventing the model from drifting away. Finding the optimal balance in hyperparameters can help in maintaining the model's performance over prolonged pretraining periods.
Regular Fine-Tuning: Periodic fine-tuning of the model on specific tasks related to the target language can help reinforce the language-specific knowledge while retaining the multilingual capabilities. This approach can prevent the model from losing its proficiency in understanding multiple languages.
Incorporating Language-Specific Data: Introducing more language-specific data from the target language during pretraining can help the model focus on the nuances and intricacies of that language while still benefiting from the multilingual pretraining. This can help in maintaining a balance between language-specific and multilingual knowledge.