Don't Just Say "I don't know"! Self-aligning Large Language Models for Responding to Unknown Questions with Explanations

• URL: http://arxiv.org/abs/2402.15062v2
• Date: Wed, 02 Oct 2024 02:09:37 GMT
• Title: Don't Just Say "I don't know"! Self-aligning Large Language Models for Responding to Unknown Questions with Explanations
• Authors: Yang Deng, Yong Zhao, Moxin Li, See-Kiong Ng, Tat-Seng Chua,
• Abstract summary: Self-alignment method is capable of not only refusing to answer but also providing explanation to the unanswerability of unknown questions. 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.
• Score: 70.6395572287422
• License:
• Abstract: 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.

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