Fine Tuning Task

Fine-tuning pre-trained large language models (LLMs) adapts their broad capabilities to specific tasks, but often leads to "catastrophic forgetting" – a loss of pre-trained knowledge. Current research focuses on parameter-efficient techniques like low-rank adaptation and pruning to mitigate this, alongside strategies for optimizing the fine-tuning process itself, including improved optimizers and data selection methods. These advancements aim to improve the efficiency and effectiveness of fine-tuning, enabling broader application of LLMs while minimizing resource consumption and preserving valuable pre-trained features. This work has significant implications for deploying LLMs in resource-constrained environments and for enhancing their generalization capabilities across diverse tasks.