Don't Just Say "I don't know"! Self-aligning Large Language Models for Responding to Unknown Questions with Explanations
<|reference_start|>Don't Just Say "I don't know"! Self-aligning Large Language Models for Responding to Unknown Questions with Explanations: 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.<|reference_end|>
@article{deng2024don't,
    title={Don't Just Say "I don't know"! Self-aligning Large Language Models for
  Responding to Unknown Questions with Explanations},
    author={Yang Deng, Yong Zhao, Moxin Li, See-Kiong Ng, Tat-Seng Chua},
    journal={arXiv preprint arXiv:2402.15062},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.15062},
    primaryClass={cs.CL cs.LG}
}