Gotcha! Don't trick me with unanswerable questions! Self-aligning Large
  Language Models for Responding to Unknown Questions
Yang Deng Yong Zhao Moxin Li See-Kiong Ng Tat-Seng Chua
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.