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.

通过 Explanation-Guided LLMs Active Distillation (ELAD) 框架，我们引入了一种主动学习策略，以优化注释成本与模型性能之间的平衡；通过利用解释步骤中的不确定性，我们改进了高效样本选择方法；此外，我们还提出了一种定制化的 LLMM - 注释解释修订技术，以检测和修正学生模型推理中的缺陷。我们在各种推理数据集上的实验证明，我们的框架显著提高了 LLMM 知识蒸馏的效率。