Data Efficient Fine Tuning

Data-efficient fine-tuning (DEFT) aims to optimize the training of large language models (LLMs) by minimizing the amount of labeled data required for achieving high performance on downstream tasks. Current research focuses on combining parameter-efficient fine-tuning methods, such as LoRA, with active learning strategies and data augmentation techniques to select the most informative subsets of training data or generate synthetic examples. This research is significant because it addresses the substantial cost and time constraints associated with acquiring and annotating large datasets, making LLMs more accessible and practical for a wider range of applications.