Instruction Augmentation

Instruction augmentation enhances the performance of large language models (LLMs) by supplementing training data with additional instructions or modifying existing ones. Current research focuses on developing methods for automatically generating these augmented instructions, exploring their impact across various modalities (text, vision, robotics), and investigating how instruction design affects model performance, particularly in low-data regimes. This research is significant because it improves LLMs' ability to generalize to new tasks and reduces the reliance on extensive, manually-labeled datasets, thereby broadening the accessibility and applicability of these powerful models.