What are the key challenges in creating LLM adaptation strategies that preserve existing capabilities?

The key challenges in creating LLM adaptation strategies that preserve existing capabilities include managing temporal distribution shifts, ensuring efficient fine-tuning without retraining from scratch, and maintaining model performance across diverse domains. These challenges arise because LLMs are often fine-tuned on static datasets, which do not account for the continuous evolution of domain knowledge, such as new regulations or products. As a result, when models are adapted to specific domains, they can experience performance degradation due to the mismatch between the training data and real-world applications. For instance, research has shown that when LLMs are fine-tuned on domain-specific data, they may lose their generalization capabilities, leading to suboptimal performance in other areas. A study by Wang et al. (2022) highlights that naive retraining approaches can lead to increased costs and inefficiencies, prompting the need for more sophisticated adaptation strategies that leverage temporal domain generalization techniques to better handle evolving data while preserving existing model capabilities.