Despite the remarkable abilities of Large Language Models (LLMs) to answer
questions, they often display a considerable level of overconfidence even when
the question does not have a definitive answer. To avoid providing hallucinated
answers to these unknown questions, existing studies typically investigate
approaches to refusing to answer these questions. In this work, we propose a
novel and scalable self-alignment method to utilize the LLM itself to enhance
its response-ability to different types of unknown questions, being capable of
not only refusing to answer but also providing explanation to the
unanswerability of unknown questions. Specifically, the Self-Align method first
employ a two-stage class-aware self-augmentation approach to generate a large
amount of unknown question-response data. Then we conduct disparity-driven
self-curation to select qualified data for fine-tuning the LLM itself for
aligning the responses to unknown questions as desired. Experimental results on
two datasets across four types of unknown questions validate the superiority of
the Self-Align method over existing baselines in terms of three types of task
formulation.

通过自体对齐方法，本文提出一种新颖且可扩展的方法以增强大型语言模型对不同类型未知问题的回答能力，不仅能够拒绝回答，还能提供关于无法回答的解释。实验结果验证了该方法在两个数据集上针对四种未知问题类型的三种任务形式方面优于现有基准模型。