To mitigate the catastrophic forgetting of language capabilities during fine-tuning on image-text datasets, several techniques can be employed:
Progressive Layer Freezing: Gradually freeze layers of the pre-trained language model during fine-tuning. This allows the model to retain its language capabilities while adapting to the new task. Initially, only the top layers are fine-tuned, and as training progresses, more layers can be unfrozen.
Regularization Techniques: Implement regularization methods such as Elastic Weight Consolidation (EWC) or L2 regularization. These techniques help to preserve important weights associated with the language capabilities by penalizing significant changes to them during fine-tuning.
Multi-Task Learning: Train the model on multiple tasks simultaneously, including both language and image-text generation tasks. This approach encourages the model to maintain its language capabilities while learning new tasks, as it has to balance the learning objectives.
Knowledge Distillation: Use knowledge distillation to transfer knowledge from the original pre-trained model to the fine-tuned model. By training a smaller model to mimic the outputs of the larger pre-trained model, we can help retain the language capabilities while adapting to new tasks.
Curriculum Learning: Implement a curriculum learning strategy where the model is first fine-tuned on simpler tasks before progressing to more complex image-text tasks. This gradual increase in complexity can help the model retain its language capabilities while learning new information.
Memory Augmentation: Incorporate memory-augmented neural networks that can store and retrieve important language-related information during fine-tuning. This allows the model to access previously learned language knowledge, reducing the risk of forgetting.
By applying these techniques, we can effectively reduce the impact of catastrophic forgetting and ensure that language models retain their capabilities while adapting to new multi-modal tasks.