From Quantity to Quality: Boosting LLM Performance with Self-Guided Data Selection for Instruction Tuning
CoRR(2023)
摘要
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
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. Codes, data, and models
are available: https://github.com/tianyi-lab/Cherry_LLM
更多查看译文
关键词
llm performance,tuning,instruction,selection,self-guided
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要