Instruction Finetuning

Instruction finetuning enhances large language models (LLMs) by training them on diverse instruction-following datasets, improving their ability to perform a wider range of tasks and generalize to unseen prompts. Current research focuses on optimizing this process through techniques like coreset selection to reduce computational costs, developing methods for selecting high-quality and diverse instruction data, and exploring personalized and federated approaches for collaborative model training. This technique significantly improves LLMs' performance across various domains, from scientific research (e.g., materials science, biomedical relation extraction) to practical applications like automated leaderboard generation and sentiment analysis, ultimately advancing both the capabilities and accessibility of LLMs.