  In the realm of Large Language Models (LLMs), the balance between instruction
data quality and quantity is a focal point. Recognizing this, we introduce a
self-guided methodology for LLMs to autonomously discern and select cherry
samples from 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 metric to
identify discrepancies between a model's expected responses and its intrinsic
generation capability. Through the application of IFD, cherry samples can be
pinpointed, leading to a marked uptick in model training efficiency. Empirical
validations on datasets like Alpaca and WizardLM underpin our findings; with a
mere $10\%$ of original data input, our strategy showcases improved results.
This synthesis of self-guided cherry-picking and the IFD metric signifies a
transformative leap in the instruction tuning of LLMs, promising both
efficiency and resource-conscious advancements. Codes, data, and models are
available: https://github.com/tianyi-lab/Cherry_LLM
