In the current landscape of large language models (LLMs), the process of
instruction tuning serves as an essential step. Considering the high computing
power overhead, data-efficient instruction tuning was proposed to reduce the
training data size in this process, aiming at selecting high-quality
instructional data. Nevertheless, we argue that most current data-efficient
instruction-tuning methods are highly dependent on the quality of the original
instruction-tuning dataset. When it comes to datasets synthesized by LLMs, a
common scenario in this field, dirty samples will even be selected with a
higher probability than other samples. To address these challenges, we utilized
external knowledge (relevant examples or paragraphs) to evaluate those samples
synthesized by LLMs with an in-context-based relative predictive entropy. Based
on the new metric, we proposed a framework, dubbed as \textbf{RECOST}, which
integrates external-knowledge-base re-ranking and diversity-consistent sampling
into a single pipeline. Through extensive experiments on several synthetic
datasets (Alpaca and Alpaca-gpt4), we demonstrate the effectiveness of our
method and achieve even better results with only \textbf{1\%} of the full
dataset.

使用外部知识与基于上下文的相对预测熵评估由大型语言模型合成的样本，并结合多样性一致性抽样，提出了一种名为 RECOST 的框架，通过在合成数据集中实验展示了方法的有效性，并仅使用全数据集的 1％即可获得更好的结果。