Instruction Paradigm

The instruction paradigm focuses on improving large language models (LLMs) by fine-tuning them with diverse instruction datasets, aiming to enhance their ability to follow complex and nuanced instructions. Current research emphasizes optimizing instruction selection and integration, exploring methods like linear programming and LLM-based selection to create efficient and high-quality training datasets, even with limited human annotation. This approach is significant because it allows for more effective and cost-efficient training of LLMs across various domains, including scientific applications, leading to improved performance on diverse tasks and reduced reliance on closed-source models.