In the realm of Large Language Models, the balance between instruction data
quality and quantity has become a focal point. Recognizing this, we introduce a
self-guided methodology for LLMs to autonomously discern and select cherry
samples from vast open-source datasets, effectively minimizing manual curation
and potential cost for instruction tuning an LLM. Our key innovation, the
Instruction-Following Difficulty (IFD) metric, emerges as a pivotal tool to
identify discrepancies between a model's expected responses and its autonomous
generation prowess. Through the adept application of IFD, cherry samples are
pinpointed, leading to a marked uptick in model training efficiency. Empirical
validations on renowned datasets like Alpaca and WizardLM underpin our
findings; with a mere 10% of conventional data input, our strategy showcases
improved results. This synthesis of self-guided cherry-picking and the IFD
metric signifies a transformative leap in the optimization of LLMs, promising
both efficiency and resource-conscious advancements.

大型语言模型的自主选择优化方法以及通过 IFD 指标识别差异以提高模型训练效率的研究对于提高 LLM 的效率和资源利用具有重要意义。