The study explores methods to balance this trade-off, including continual learning regularization methods, the weight averaging method Wise-FT, and Low-Rank Adaptation (LoRA). While continual learning methods do mitigate some generality loss, Wise-FT offers an optimal balance between maintaining generality and achieving task-specific speciality. The effectiveness of LoRA varies based on the complexity of the fine-tuning task. The research acknowledges unexplored methodologies and emphasizes the need to understand the dynamics of foundation models for future studies in Natural Language Generation.
Key takeaways:
- The balance between "speciality" and "generality" in fine-tuning foundation models, Vision Language Models (VLMs) and Large Language Models (LLMs), impacts their performance and adaptability across diverse tasks and distributions.
- While fine-tuning often enhances performance for specific tasks, it may compromise the model’s overarching generality, leading to "catastrophic forgetting" where models underperform in previously learned tasks.
- Techniques like continual learning, Wise-FT, and Low-Rank Adaptation (LoRA) were explored to mediate the trade-off between speciality and generality. Wise-FT was found to offer an optimal balance.
- The research acknowledges that certain methodologies remain unexplored and underscores the importance of understanding the dynamics of foundation models for the future of Natural Language Generation.