The deployment and application of Large Language Models (LLMs) is hindered by their memory inefficiency, computational demands, and the high costs of API inferences. Traditional distillation methods, which transfer the capabilities of LLMs to smaller models, often fail to determine whether the knowledge has been sufficiently transferred, potentially resulting in high costs or incomplete distillation. In this paper, we propose an Explanation-Guided LLMs Active Distillation (ELAD) framework that employs an active learning strategy to optimize the balance between annotation costs and model performance. To improve efficient sample selection, we introduce an explanation-guided sample selection method that identifies samples challenging its reasoning by exploiting uncertainties in explanation steps. Additionally, we present a customized LLM-annotated explanation revision technique where the teacher model detects and corrects flaws in the student model's reasoning. Our experiments across various reasoning datasets demonstrate that our framework significantly enhances the efficiency of LLM knowledge distillation.

翻译：大语言模型（LLM）的部署与应用受限于其内存低效、计算需求高以及API推理的高昂成本。传统蒸馏方法虽能将LLM的能力迁移至更小的模型，但通常无法判断知识是否已充分传递，可能导致高成本或蒸馏不完整。本文提出一种解释引导的大语言模型主动蒸馏（ELAD）框架，该框架采用主动学习策略来优化标注成本与模型性能之间的平衡。为提升样本选择的效率，我们引入一种基于解释引导的样本选择方法，通过利用解释步骤中的不确定性来识别挑战模型推理的样本。此外，我们提出一种定制的LLM标注解释修正技术，使教师模型能够检测并纠正学生模型推理中的缺陷。我们在多个推理数据集上的实验表明，该框架显著提升了LLM知识蒸馏的效率。