Instruction Tuning

Instruction tuning refines large language models (LLMs) by training them on datasets of instructions and desired responses, improving their ability to follow diverse commands and generate helpful outputs. Current research emphasizes improving data quality and diversity through techniques like data partitioning, synthetic data generation, and novel prompting strategies, often applied to various model architectures including LLMs and multimodal models. This area is significant because it directly addresses the limitations of pre-trained LLMs, leading to safer, more reliable, and more useful AI systems across numerous applications, from chatbots to specialized tools for medical diagnosis and remote sensing.