To better preserve the model's short-context capabilities while extending the context length, several strategies can be implemented:
Multi-Task Learning: Incorporating multi-task learning during training can help the model learn to perform well on both short and long-context tasks simultaneously. By training the model on a diverse set of tasks with varying context lengths, it can maintain proficiency across different scenarios.
Progressive Training: Gradually increasing the context length during training can help the model adapt to longer contexts while retaining its ability to handle shorter contexts effectively. This progressive training approach allows the model to learn incrementally and adjust its attention mechanisms accordingly.
Fine-Tuning Strategies: Implementing fine-tuning strategies that focus on specific aspects of the model related to short-context understanding can help in preserving this capability. By fine-tuning on tasks that specifically require short-context comprehension, the model can retain its proficiency in handling such inputs.
Regularization Techniques: Applying regularization techniques such as dropout or weight decay can prevent the model from overfitting to the longer contexts during training. By introducing regularization, the model's capacity to memorize long sequences is controlled, preserving its ability to generalize to shorter inputs.
Task-Specific Architectures: Designing task-specific architectures that are optimized for both short and long-context tasks can help in balancing the model's capabilities. By tailoring the architecture to the requirements of different tasks, the model can maintain its performance across varying context lengths.
By incorporating these strategies into the training and fine-tuning process, the model can better preserve its short-context capabilities while extending the context length, as observed in the MMLU results.